The data consists of 1567 datapoints each with 591 features. The dataset presented in this case represents a selection of such features where each example represents a single production entity with associated measured features and the labels represent a simple pass/fail yield for in house line testing. Target column “ –1” corresponds to a pass and “1” corresponds to a fail and the data time stamp is for that specific test point.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
signal_df = pd.read_csv('signal-data.csv')
signal_df.head()
| Time | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ... | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2008-07-19 11:55:00 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 100.0 | 97.6133 | 0.1242 | 1.5005 | ... | NaN | 0.5005 | 0.0118 | 0.0035 | 2.3630 | NaN | NaN | NaN | NaN | -1 |
| 1 | 2008-07-19 12:32:00 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 100.0 | 102.3433 | 0.1247 | 1.4966 | ... | 208.2045 | 0.5019 | 0.0223 | 0.0055 | 4.4447 | 0.0096 | 0.0201 | 0.0060 | 208.2045 | -1 |
| 2 | 2008-07-19 13:17:00 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 100.0 | 95.4878 | 0.1241 | 1.4436 | ... | 82.8602 | 0.4958 | 0.0157 | 0.0039 | 3.1745 | 0.0584 | 0.0484 | 0.0148 | 82.8602 | 1 |
| 3 | 2008-07-19 14:43:00 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 100.0 | 104.2367 | 0.1217 | 1.4882 | ... | 73.8432 | 0.4990 | 0.0103 | 0.0025 | 2.0544 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | -1 |
| 4 | 2008-07-19 15:22:00 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.0 | 100.3967 | 0.1235 | 1.5031 | ... | NaN | 0.4800 | 0.4766 | 0.1045 | 99.3032 | 0.0202 | 0.0149 | 0.0044 | 73.8432 | -1 |
5 rows × 592 columns
signal_df.shape
(1567, 592)
signal_df.columns
Index(['Time', '0', '1', '2', '3', '4', '5', '6', '7', '8',
...
'581', '582', '583', '584', '585', '586', '587', '588', '589',
'Pass/Fail'],
dtype='object', length=592)
signal_df.dtypes
Time object
0 float64
1 float64
2 float64
3 float64
...
586 float64
587 float64
588 float64
589 float64
Pass/Fail int64
Length: 592, dtype: object
signal_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1567 entries, 0 to 1566 Columns: 592 entries, Time to Pass/Fail dtypes: float64(590), int64(1), object(1) memory usage: 7.1+ MB
signal_df.describe()
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 581 | 582 | 583 | 584 | 585 | 586 | 587 | 588 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1561.000000 | 1560.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.0 | 1553.000000 | 1558.000000 | 1565.000000 | 1565.000000 | ... | 618.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1567.000000 |
| mean | 3014.452896 | 2495.850231 | 2200.547318 | 1396.376627 | 4.197013 | 100.0 | 101.112908 | 0.121822 | 1.462862 | -0.000841 | ... | 97.934373 | 0.500096 | 0.015318 | 0.003847 | 3.067826 | 0.021458 | 0.016475 | 0.005283 | 99.670066 | -0.867262 |
| std | 73.621787 | 80.407705 | 29.513152 | 441.691640 | 56.355540 | 0.0 | 6.237214 | 0.008961 | 0.073897 | 0.015116 | ... | 87.520966 | 0.003404 | 0.017180 | 0.003720 | 3.578033 | 0.012358 | 0.008808 | 0.002867 | 93.891919 | 0.498010 |
| min | 2743.240000 | 2158.750000 | 2060.660000 | 0.000000 | 0.681500 | 100.0 | 82.131100 | 0.000000 | 1.191000 | -0.053400 | ... | 0.000000 | 0.477800 | 0.006000 | 0.001700 | 1.197500 | -0.016900 | 0.003200 | 0.001000 | 0.000000 | -1.000000 |
| 25% | 2966.260000 | 2452.247500 | 2181.044400 | 1081.875800 | 1.017700 | 100.0 | 97.920000 | 0.121100 | 1.411200 | -0.010800 | ... | 46.184900 | 0.497900 | 0.011600 | 0.003100 | 2.306500 | 0.013425 | 0.010600 | 0.003300 | 44.368600 | -1.000000 |
| 50% | 3011.490000 | 2499.405000 | 2201.066700 | 1285.214400 | 1.316800 | 100.0 | 101.512200 | 0.122400 | 1.461600 | -0.001300 | ... | 72.288900 | 0.500200 | 0.013800 | 0.003600 | 2.757650 | 0.020500 | 0.014800 | 0.004600 | 71.900500 | -1.000000 |
| 75% | 3056.650000 | 2538.822500 | 2218.055500 | 1591.223500 | 1.525700 | 100.0 | 104.586700 | 0.123800 | 1.516900 | 0.008400 | ... | 116.539150 | 0.502375 | 0.016500 | 0.004100 | 3.295175 | 0.027600 | 0.020300 | 0.006400 | 114.749700 | -1.000000 |
| max | 3356.350000 | 2846.440000 | 2315.266700 | 3715.041700 | 1114.536600 | 100.0 | 129.252200 | 0.128600 | 1.656400 | 0.074900 | ... | 737.304800 | 0.509800 | 0.476600 | 0.104500 | 99.303200 | 0.102800 | 0.079900 | 0.028600 | 737.304800 | 1.000000 |
8 rows × 591 columns
signal_df.isna().sum()
Time 0
0 6
1 7
2 14
3 14
..
586 1
587 1
588 1
589 1
Pass/Fail 0
Length: 592, dtype: int64
signal_df['Pass/Fail'].unique()
array([-1, 1], dtype=int64)
signal_df['Pass/Fail'].isna().sum()
0
signal_df.describe().T.style
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1561.000000 | 3014.452896 | 73.621787 | 2743.240000 | 2966.260000 | 3011.490000 | 3056.650000 | 3356.350000 |
| 1 | 1560.000000 | 2495.850231 | 80.407705 | 2158.750000 | 2452.247500 | 2499.405000 | 2538.822500 | 2846.440000 |
| 2 | 1553.000000 | 2200.547318 | 29.513152 | 2060.660000 | 2181.044400 | 2201.066700 | 2218.055500 | 2315.266700 |
| 3 | 1553.000000 | 1396.376627 | 441.691640 | 0.000000 | 1081.875800 | 1285.214400 | 1591.223500 | 3715.041700 |
| 4 | 1553.000000 | 4.197013 | 56.355540 | 0.681500 | 1.017700 | 1.316800 | 1.525700 | 1114.536600 |
| 5 | 1553.000000 | 100.000000 | 0.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 | 100.000000 |
| 6 | 1553.000000 | 101.112908 | 6.237214 | 82.131100 | 97.920000 | 101.512200 | 104.586700 | 129.252200 |
| 7 | 1558.000000 | 0.121822 | 0.008961 | 0.000000 | 0.121100 | 0.122400 | 0.123800 | 0.128600 |
| 8 | 1565.000000 | 1.462862 | 0.073897 | 1.191000 | 1.411200 | 1.461600 | 1.516900 | 1.656400 |
| 9 | 1565.000000 | -0.000841 | 0.015116 | -0.053400 | -0.010800 | -0.001300 | 0.008400 | 0.074900 |
| 10 | 1565.000000 | 0.000146 | 0.009302 | -0.034900 | -0.005600 | 0.000400 | 0.005900 | 0.053000 |
| 11 | 1565.000000 | 0.964353 | 0.012452 | 0.655400 | 0.958100 | 0.965800 | 0.971300 | 0.984800 |
| 12 | 1565.000000 | 199.956809 | 3.257276 | 182.094000 | 198.130700 | 199.535600 | 202.007100 | 272.045100 |
| 13 | 1564.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 14 | 1564.000000 | 9.005371 | 2.796596 | 2.249300 | 7.094875 | 8.967000 | 10.861875 | 19.546500 |
| 15 | 1564.000000 | 413.086035 | 17.221095 | 333.448600 | 406.127400 | 412.219100 | 419.089275 | 824.927100 |
| 16 | 1564.000000 | 9.907603 | 2.403867 | 4.469600 | 9.567625 | 9.851750 | 10.128175 | 102.867700 |
| 17 | 1564.000000 | 0.971444 | 0.012062 | 0.579400 | 0.968200 | 0.972600 | 0.976800 | 0.984800 |
| 18 | 1564.000000 | 190.047354 | 2.781041 | 169.177400 | 188.299825 | 189.664200 | 192.189375 | 215.597700 |
| 19 | 1557.000000 | 12.481034 | 0.217965 | 9.877300 | 12.460000 | 12.499600 | 12.547100 | 12.989800 |
| 20 | 1567.000000 | 1.405054 | 0.016737 | 1.179700 | 1.396500 | 1.406000 | 1.415000 | 1.453400 |
| 21 | 1565.000000 | -5618.393610 | 626.822178 | -7150.250000 | -5933.250000 | -5523.250000 | -5356.250000 | 0.000000 |
| 22 | 1565.000000 | 2699.378435 | 295.498535 | 0.000000 | 2578.000000 | 2664.000000 | 2841.750000 | 3656.250000 |
| 23 | 1565.000000 | -3806.299734 | 1380.162148 | -9986.750000 | -4371.750000 | -3820.750000 | -3352.750000 | 2363.000000 |
| 24 | 1565.000000 | -298.598136 | 2902.690117 | -14804.500000 | -1476.000000 | -78.750000 | 1377.250000 | 14106.000000 |
| 25 | 1565.000000 | 1.203845 | 0.177600 | 0.000000 | 1.094800 | 1.283000 | 1.304300 | 1.382800 |
| 26 | 1565.000000 | 1.938477 | 0.189495 | 0.000000 | 1.906500 | 1.986500 | 2.003200 | 2.052800 |
| 27 | 1565.000000 | 6.638628 | 1.244249 | 0.000000 | 5.263700 | 7.264700 | 7.329700 | 7.658800 |
| 28 | 1565.000000 | 69.499532 | 3.461181 | 59.400000 | 67.377800 | 69.155600 | 72.266700 | 77.900000 |
| 29 | 1565.000000 | 2.366197 | 0.408694 | 0.666700 | 2.088900 | 2.377800 | 2.655600 | 3.511100 |
| 30 | 1565.000000 | 0.184159 | 0.032944 | 0.034100 | 0.161700 | 0.186700 | 0.207100 | 0.285100 |
| 31 | 1565.000000 | 3.673189 | 0.535322 | 2.069800 | 3.362700 | 3.431000 | 3.531300 | 4.804400 |
| 32 | 1566.000000 | 85.337469 | 2.026549 | 83.182900 | 84.490500 | 85.135450 | 85.741900 | 105.603800 |
| 33 | 1566.000000 | 8.960279 | 1.344456 | 7.603200 | 8.580000 | 8.769800 | 9.060600 | 23.345300 |
| 34 | 1566.000000 | 50.582639 | 1.182618 | 49.834800 | 50.252350 | 50.396400 | 50.578800 | 59.771100 |
| 35 | 1566.000000 | 64.555787 | 2.574749 | 63.677400 | 64.024800 | 64.165800 | 64.344700 | 94.264100 |
| 36 | 1566.000000 | 49.417370 | 1.182619 | 40.228900 | 49.421200 | 49.603600 | 49.747650 | 50.165200 |
| 37 | 1566.000000 | 66.221274 | 0.304141 | 64.919300 | 66.040650 | 66.231800 | 66.343275 | 67.958600 |
| 38 | 1566.000000 | 86.836577 | 0.446756 | 84.732700 | 86.578300 | 86.820700 | 87.002400 | 88.418800 |
| 39 | 1566.000000 | 118.679554 | 1.807221 | 111.712800 | 118.015600 | 118.399300 | 118.939600 | 133.389800 |
| 40 | 1543.000000 | 67.904909 | 24.062943 | 1.434000 | 74.800000 | 78.290000 | 80.200000 | 86.120000 |
| 41 | 1543.000000 | 3.353066 | 2.360425 | -0.075900 | 2.690000 | 3.074000 | 3.521000 | 37.880000 |
| 42 | 1566.000000 | 70.000000 | 0.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 | 70.000000 |
| 43 | 1566.000000 | 355.538904 | 6.234706 | 342.754500 | 350.801575 | 353.720900 | 360.772250 | 377.297300 |
| 44 | 1566.000000 | 10.031165 | 0.175038 | 9.464000 | 9.925425 | 10.034850 | 10.152475 | 11.053000 |
| 45 | 1566.000000 | 136.743060 | 7.849247 | 108.846400 | 130.728875 | 136.400000 | 142.098225 | 176.313600 |
| 46 | 1566.000000 | 733.672811 | 12.170315 | 699.813900 | 724.442300 | 733.450000 | 741.454500 | 789.752300 |
| 47 | 1566.000000 | 1.177958 | 0.189637 | 0.496700 | 0.985000 | 1.251050 | 1.340350 | 1.511100 |
| 48 | 1566.000000 | 139.972231 | 4.524251 | 125.798200 | 136.926800 | 140.007750 | 143.195700 | 163.250900 |
| 49 | 1566.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 50 | 1566.000000 | 632.254197 | 8.643985 | 607.392700 | 625.928425 | 631.370900 | 638.136325 | 667.741800 |
| 51 | 1566.000000 | 157.420991 | 60.925108 | 40.261400 | 115.508975 | 183.318150 | 206.977150 | 258.543200 |
| 52 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 53 | 1563.000000 | 4.592971 | 0.054950 | 3.706000 | 4.574000 | 4.596000 | 4.617000 | 4.764000 |
| 54 | 1563.000000 | 4.838523 | 0.059581 | 3.932000 | 4.816000 | 4.843000 | 4.869000 | 5.011000 |
| 55 | 1563.000000 | 2856.172105 | 25.749317 | 2801.000000 | 2836.000000 | 2854.000000 | 2874.000000 | 2936.000000 |
| 56 | 1563.000000 | 0.928849 | 0.006807 | 0.875500 | 0.925450 | 0.931000 | 0.933100 | 0.937800 |
| 57 | 1563.000000 | 0.949215 | 0.004176 | 0.931900 | 0.946650 | 0.949300 | 0.952000 | 0.959800 |
| 58 | 1563.000000 | 4.593312 | 0.085095 | 4.219900 | 4.531900 | 4.572700 | 4.668600 | 4.847500 |
| 59 | 1560.000000 | 2.960241 | 9.532220 | -28.988200 | -1.871575 | 0.947250 | 4.385225 | 168.145500 |
| 60 | 1561.000000 | 355.159094 | 6.027889 | 324.714500 | 350.596400 | 353.799100 | 359.673600 | 373.866400 |
| 61 | 1561.000000 | 10.423143 | 0.274877 | 9.461100 | 10.283000 | 10.436700 | 10.591600 | 11.784900 |
| 62 | 1561.000000 | 116.502329 | 8.629022 | 81.490000 | 112.022700 | 116.211800 | 120.927300 | 287.150900 |
| 63 | 1560.000000 | 13.989927 | 7.119863 | 1.659100 | 10.364300 | 13.246050 | 16.376100 | 188.092300 |
| 64 | 1560.000000 | 20.542109 | 4.977467 | 6.448200 | 17.364800 | 20.021350 | 22.813625 | 48.988200 |
| 65 | 1560.000000 | 27.131816 | 7.121703 | 4.308000 | 23.056425 | 26.261450 | 29.914950 | 118.083600 |
| 66 | 1561.000000 | 706.668523 | 11.623078 | 632.422600 | 698.770200 | 706.453600 | 714.597000 | 770.608400 |
| 67 | 1561.000000 | 16.715444 | 307.502293 | 0.413700 | 0.890700 | 0.978300 | 1.065000 | 7272.828300 |
| 68 | 1561.000000 | 147.437578 | 4.240095 | 87.025500 | 145.237300 | 147.597300 | 149.959100 | 167.830900 |
| 69 | 1561.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 70 | 1561.000000 | 619.101687 | 9.539190 | 581.777300 | 612.774500 | 619.032700 | 625.170000 | 722.601800 |
| 71 | 1561.000000 | 104.329033 | 31.651899 | 21.433200 | 87.484200 | 102.604300 | 115.498900 | 238.477500 |
| 72 | 773.000000 | 150.361552 | 18.388481 | -59.477700 | 145.305300 | 152.297200 | 158.437800 | 175.413200 |
| 73 | 773.000000 | 468.020404 | 17.629886 | 456.044700 | 464.458100 | 466.081700 | 467.889900 | 692.425600 |
| 74 | 1561.000000 | 0.002688 | 0.106190 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.195500 |
| 75 | 1543.000000 | -0.006903 | 0.022292 | -0.104900 | -0.019550 | -0.006300 | 0.007100 | 0.231500 |
| 76 | 1543.000000 | -0.029390 | 0.033203 | -0.186200 | -0.051900 | -0.028900 | -0.006500 | 0.072300 |
| 77 | 1543.000000 | -0.007041 | 0.031368 | -0.104600 | -0.029500 | -0.009900 | 0.009250 | 0.133100 |
| 78 | 1543.000000 | -0.013643 | 0.047872 | -0.348200 | -0.047600 | -0.012500 | 0.012200 | 0.249200 |
| 79 | 1543.000000 | 0.003458 | 0.023080 | -0.056800 | -0.010800 | 0.000600 | 0.013200 | 0.101300 |
| 80 | 1543.000000 | -0.018531 | 0.049226 | -0.143700 | -0.044500 | -0.008700 | 0.009100 | 0.118600 |
| 81 | 1543.000000 | -0.021153 | 0.017021 | -0.098200 | -0.027200 | -0.019600 | -0.012000 | 0.058400 |
| 82 | 1543.000000 | 0.006055 | 0.036074 | -0.212900 | -0.018000 | 0.007600 | 0.026900 | 0.143700 |
| 83 | 1566.000000 | 7.452067 | 0.516251 | 5.825700 | 7.104225 | 7.467450 | 7.807625 | 8.990400 |
| 84 | 1555.000000 | 0.133108 | 0.005051 | 0.117400 | 0.129800 | 0.133000 | 0.136300 | 0.150500 |
| 85 | 226.000000 | 0.112783 | 0.002928 | 0.105300 | 0.110725 | 0.113550 | 0.114900 | 0.118400 |
| 86 | 1567.000000 | 2.401872 | 0.037332 | 2.242500 | 2.376850 | 2.403900 | 2.428600 | 2.555500 |
| 87 | 1567.000000 | 0.982420 | 0.012848 | 0.774900 | 0.975800 | 0.987400 | 0.989700 | 0.993500 |
| 88 | 1567.000000 | 1807.815021 | 53.537262 | 1627.471400 | 1777.470300 | 1809.249200 | 1841.873000 | 2105.182300 |
| 89 | 1516.000000 | 0.188703 | 0.052373 | 0.111300 | 0.169375 | 0.190100 | 0.200425 | 1.472700 |
| 90 | 1516.000000 | 8827.536865 | 396.313662 | 7397.310000 | 8564.689975 | 8825.435100 | 9065.432400 | 10746.600000 |
| 91 | 1561.000000 | 0.002440 | 0.087683 | -0.357000 | -0.042900 | 0.000000 | 0.050700 | 0.362700 |
| 92 | 1565.000000 | 0.000507 | 0.003231 | -0.012600 | -0.001200 | 0.000400 | 0.002000 | 0.028100 |
| 93 | 1565.000000 | -0.000541 | 0.003010 | -0.017100 | -0.001600 | -0.000200 | 0.001000 | 0.013300 |
| 94 | 1561.000000 | -0.000029 | 0.000174 | -0.002000 | -0.000100 | 0.000000 | 0.000100 | 0.001100 |
| 95 | 1561.000000 | 0.000060 | 0.000104 | -0.000900 | 0.000000 | 0.000000 | 0.000100 | 0.000900 |
| 96 | 1561.000000 | 0.017127 | 0.219578 | -1.480300 | -0.088600 | 0.003900 | 0.122000 | 2.509300 |
| 97 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 98 | 1561.000000 | -0.018143 | 0.427110 | -5.271700 | -0.218800 | 0.000000 | 0.189300 | 2.569800 |
| 99 | 1561.000000 | 0.001540 | 0.062740 | -0.528300 | -0.029800 | 0.000000 | 0.029800 | 0.885400 |
| 100 | 1561.000000 | -0.000021 | 0.000356 | -0.003000 | -0.000200 | 0.000000 | 0.000200 | 0.002300 |
| 101 | 1561.000000 | -0.000007 | 0.000221 | -0.002400 | -0.000100 | 0.000000 | 0.000100 | 0.001700 |
| 102 | 1561.000000 | 0.001115 | 0.062968 | -0.535300 | -0.035700 | 0.000000 | 0.033600 | 0.297900 |
| 103 | 1565.000000 | -0.009789 | 0.003065 | -0.032900 | -0.011800 | -0.010100 | -0.008200 | 0.020300 |
| 104 | 1565.000000 | -0.000015 | 0.000851 | -0.011900 | -0.000400 | 0.000000 | 0.000400 | 0.007100 |
| 105 | 1561.000000 | -0.000498 | 0.003202 | -0.028100 | -0.001900 | -0.000200 | 0.001100 | 0.012700 |
| 106 | 1561.000000 | 0.000540 | 0.002988 | -0.013300 | -0.001000 | 0.000200 | 0.001600 | 0.017200 |
| 107 | 1561.000000 | -0.001766 | 0.087475 | -0.522600 | -0.048600 | 0.000000 | 0.049000 | 0.485600 |
| 108 | 1561.000000 | -0.010789 | 0.086758 | -0.345400 | -0.064900 | -0.011200 | 0.038000 | 0.393800 |
| 109 | 549.000000 | 0.979993 | 0.008695 | 0.784800 | 0.978800 | 0.981000 | 0.982300 | 0.984200 |
| 110 | 549.000000 | 101.318253 | 1.880087 | 88.193800 | 100.389000 | 101.481700 | 102.078100 | 106.922700 |
| 111 | 549.000000 | 231.818898 | 2.105318 | 213.008300 | 230.373800 | 231.201200 | 233.036100 | 236.954600 |
| 112 | 852.000000 | 0.457538 | 0.048939 | 0.000000 | 0.459300 | 0.462850 | 0.466425 | 0.488500 |
| 113 | 1567.000000 | 0.945424 | 0.012133 | 0.853400 | 0.938600 | 0.946400 | 0.952300 | 0.976300 |
| 114 | 1567.000000 | 0.000123 | 0.001668 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.041400 |
| 115 | 1567.000000 | 747.383792 | 48.949250 | 544.025400 | 721.023000 | 750.861400 | 776.781850 | 924.531800 |
| 116 | 1567.000000 | 0.987130 | 0.009497 | 0.890000 | 0.989500 | 0.990500 | 0.990900 | 0.992400 |
| 117 | 1567.000000 | 58.625908 | 6.485174 | 52.806800 | 57.978300 | 58.549100 | 59.133900 | 311.734400 |
| 118 | 1543.000000 | 0.598412 | 0.008102 | 0.527400 | 0.594100 | 0.599000 | 0.603400 | 0.624500 |
| 119 | 1567.000000 | 0.970777 | 0.008949 | 0.841100 | 0.964800 | 0.969400 | 0.978300 | 0.982700 |
| 120 | 1567.000000 | 6.310863 | 0.124304 | 5.125900 | 6.246400 | 6.313600 | 6.375850 | 7.522000 |
| 121 | 1558.000000 | 15.796425 | 0.099618 | 15.460000 | 15.730000 | 15.790000 | 15.860000 | 16.070000 |
| 122 | 1558.000000 | 3.898390 | 0.904120 | 1.671000 | 3.202000 | 3.877000 | 4.392000 | 6.889000 |
| 123 | 1558.000000 | 15.829660 | 0.108315 | 15.170000 | 15.762500 | 15.830000 | 15.900000 | 16.100000 |
| 124 | 1558.000000 | 15.794705 | 0.114144 | 15.430000 | 15.722500 | 15.780000 | 15.870000 | 16.100000 |
| 125 | 1558.000000 | 1.184956 | 0.280555 | 0.312200 | 0.974400 | 1.144000 | 1.338000 | 2.465000 |
| 126 | 1558.000000 | 2.750728 | 0.253471 | 2.340000 | 2.572000 | 2.735000 | 2.873000 | 3.991000 |
| 127 | 1558.000000 | 0.648478 | 0.135409 | 0.316100 | 0.548900 | 0.653900 | 0.713500 | 1.175000 |
| 128 | 1558.000000 | 3.192182 | 0.264175 | 0.000000 | 3.074000 | 3.195000 | 3.311000 | 3.895000 |
| 129 | 1558.000000 | -0.554228 | 1.220479 | -3.779000 | -0.898800 | -0.141900 | 0.047300 | 2.458000 |
| 130 | 1558.000000 | 0.744976 | 0.082531 | 0.419900 | 0.688700 | 0.758750 | 0.814500 | 0.888400 |
| 131 | 1558.000000 | 0.997808 | 0.002251 | 0.993600 | 0.996400 | 0.997750 | 0.998900 | 1.019000 |
| 132 | 1559.000000 | 2.318545 | 0.053181 | 2.191100 | 2.277300 | 2.312400 | 2.358300 | 2.472300 |
| 133 | 1559.000000 | 1004.043093 | 6.537701 | 980.451000 | 999.996100 | 1004.050000 | 1008.670600 | 1020.994400 |
| 134 | 1559.000000 | 39.391979 | 2.990476 | 33.365800 | 37.347250 | 38.902600 | 40.804600 | 64.128700 |
| 135 | 1562.000000 | 117.960948 | 57.544627 | 58.000000 | 92.000000 | 109.000000 | 127.000000 | 994.000000 |
| 136 | 1561.000000 | 138.194747 | 53.909792 | 36.100000 | 90.000000 | 134.600000 | 181.000000 | 295.800000 |
| 137 | 1560.000000 | 122.692949 | 52.253015 | 19.200000 | 81.300000 | 117.700000 | 161.600000 | 334.700000 |
| 138 | 1553.000000 | 57.603025 | 12.345358 | 19.800000 | 50.900100 | 55.900100 | 62.900100 | 141.799800 |
| 139 | 1553.000000 | 416.766964 | 263.300614 | 0.000000 | 243.786000 | 339.561000 | 502.205900 | 1770.690900 |
| 140 | 1553.000000 | 26.077904 | 506.922106 | 0.031900 | 0.131700 | 0.235800 | 0.439100 | 9998.894400 |
| 141 | 1553.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 142 | 1553.000000 | 6.641565 | 3.552254 | 1.740000 | 5.110000 | 6.260000 | 7.500000 | 103.390000 |
| 143 | 1558.000000 | 0.004169 | 0.001282 | 0.000000 | 0.003300 | 0.003900 | 0.004900 | 0.012100 |
| 144 | 1565.000000 | 0.120008 | 0.061343 | 0.032400 | 0.083900 | 0.107500 | 0.132700 | 0.625300 |
| 145 | 1565.000000 | 0.063621 | 0.026541 | 0.021400 | 0.048000 | 0.058600 | 0.071800 | 0.250700 |
| 146 | 1565.000000 | 0.055010 | 0.021844 | 0.022700 | 0.042300 | 0.050000 | 0.061500 | 0.247900 |
| 147 | 1565.000000 | 0.017411 | 0.027123 | 0.004300 | 0.010000 | 0.015900 | 0.021300 | 0.978300 |
| 148 | 1565.000000 | 8.471308 | 18.740631 | 1.420800 | 6.359900 | 7.917300 | 9.585300 | 742.942100 |
| 149 | 1564.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 150 | 1564.000000 | 6.814268 | 3.241843 | 1.337000 | 4.459250 | 5.951000 | 8.275000 | 22.318000 |
| 151 | 1564.000000 | 14.047403 | 31.002541 | 2.020000 | 8.089750 | 10.993500 | 14.347250 | 536.564000 |
| 152 | 1564.000000 | 1.196733 | 23.364063 | 0.154400 | 0.373750 | 0.468700 | 0.679925 | 924.378000 |
| 153 | 1564.000000 | 0.011926 | 0.009346 | 0.003600 | 0.007275 | 0.011100 | 0.014900 | 0.238900 |
| 154 | 1564.000000 | 7.697971 | 5.239219 | 1.243800 | 5.926950 | 7.512700 | 9.054675 | 191.547800 |
| 155 | 1557.000000 | 0.507171 | 1.122427 | 0.140000 | 0.240000 | 0.320000 | 0.450000 | 12.710000 |
| 156 | 1567.000000 | 0.058089 | 0.079174 | 0.011100 | 0.036250 | 0.048700 | 0.066700 | 2.201600 |
| 157 | 138.000000 | 0.047104 | 0.039538 | 0.011800 | 0.027050 | 0.035450 | 0.048875 | 0.287600 |
| 158 | 138.000000 | 1039.650738 | 406.848810 | 234.099600 | 721.675050 | 1020.300050 | 1277.750125 | 2505.299800 |
| 159 | 1565.000000 | 882.680511 | 983.043021 | 0.000000 | 411.000000 | 623.000000 | 966.000000 | 7791.000000 |
| 160 | 1565.000000 | 555.346326 | 574.808588 | 0.000000 | 295.000000 | 438.000000 | 625.000000 | 4170.000000 |
| 161 | 1565.000000 | 4066.850479 | 4239.245058 | 0.000000 | 1321.000000 | 2614.000000 | 5034.000000 | 37943.000000 |
| 162 | 1565.000000 | 4797.154633 | 6553.569317 | 0.000000 | 451.000000 | 1784.000000 | 6384.000000 | 36871.000000 |
| 163 | 1565.000000 | 0.140204 | 0.121989 | 0.000000 | 0.091000 | 0.120000 | 0.154000 | 0.957000 |
| 164 | 1565.000000 | 0.127942 | 0.242534 | 0.000000 | 0.068000 | 0.089000 | 0.116000 | 1.817000 |
| 165 | 1565.000000 | 0.252026 | 0.407329 | 0.000000 | 0.132000 | 0.184000 | 0.255000 | 3.286000 |
| 166 | 1565.000000 | 2.788882 | 1.119756 | 0.800000 | 2.100000 | 2.600000 | 3.200000 | 21.100000 |
| 167 | 1565.000000 | 1.235783 | 0.632767 | 0.300000 | 0.900000 | 1.200000 | 1.500000 | 16.300000 |
| 168 | 1565.000000 | 0.124397 | 0.047639 | 0.033000 | 0.090000 | 0.119000 | 0.151000 | 0.725000 |
| 169 | 1565.000000 | 0.400454 | 0.197918 | 0.046000 | 0.230000 | 0.412000 | 0.536000 | 1.143000 |
| 170 | 1566.000000 | 0.684330 | 0.157468 | 0.297900 | 0.575600 | 0.686000 | 0.797300 | 1.153000 |
| 171 | 1566.000000 | 0.120064 | 0.060785 | 0.008900 | 0.079800 | 0.112500 | 0.140300 | 0.494000 |
| 172 | 1566.000000 | 0.320113 | 0.071243 | 0.128700 | 0.276600 | 0.323850 | 0.370200 | 0.548400 |
| 173 | 1566.000000 | 0.576192 | 0.095734 | 0.253800 | 0.516800 | 0.577600 | 0.634500 | 0.864300 |
| 174 | 1566.000000 | 0.320113 | 0.071247 | 0.128700 | 0.276500 | 0.323850 | 0.370200 | 0.548400 |
| 175 | 1566.000000 | 0.778044 | 0.116322 | 0.461600 | 0.692200 | 0.768200 | 0.843900 | 1.172000 |
| 176 | 1566.000000 | 0.244718 | 0.074918 | 0.073500 | 0.196250 | 0.242900 | 0.293925 | 0.441100 |
| 177 | 1566.000000 | 0.394760 | 0.282903 | 0.047000 | 0.222000 | 0.299000 | 0.423000 | 1.858000 |
| 178 | 1543.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 179 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 180 | 1566.000000 | 19.013257 | 3.311632 | 9.400000 | 16.850000 | 18.690000 | 20.972500 | 48.670000 |
| 181 | 1566.000000 | 0.546770 | 0.224402 | 0.093000 | 0.378000 | 0.524000 | 0.688750 | 3.573000 |
| 182 | 1566.000000 | 10.780543 | 4.164051 | 3.170000 | 7.732500 | 10.170000 | 13.337500 | 55.000000 |
| 183 | 1566.000000 | 26.661170 | 6.836101 | 5.014000 | 21.171500 | 27.200500 | 31.687000 | 72.947000 |
| 184 | 1566.000000 | 0.144815 | 0.110198 | 0.029700 | 0.102200 | 0.132600 | 0.169150 | 3.228300 |
| 185 | 1566.000000 | 7.365741 | 7.188720 | 1.940000 | 5.390000 | 6.735000 | 8.450000 | 267.910000 |
| 186 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 187 | 1566.000000 | 17.936290 | 8.609912 | 6.220000 | 14.505000 | 17.865000 | 20.860000 | 307.930000 |
| 188 | 1566.000000 | 43.211418 | 21.711876 | 6.613000 | 24.711000 | 40.209500 | 57.674750 | 191.830000 |
| 189 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 190 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 191 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 192 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 193 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 194 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 195 | 1563.000000 | 0.287084 | 0.395187 | 0.080000 | 0.218000 | 0.259000 | 0.296000 | 4.838000 |
| 196 | 1560.000000 | 8.688487 | 15.720926 | 1.750000 | 5.040000 | 6.780000 | 9.555000 | 396.110000 |
| 197 | 1561.000000 | 20.092710 | 10.552162 | 9.220000 | 17.130000 | 19.370000 | 21.460000 | 252.870000 |
| 198 | 1561.000000 | 0.557359 | 0.537705 | 0.090000 | 0.296000 | 0.424000 | 0.726000 | 10.017000 |
| 199 | 1561.000000 | 11.532056 | 16.445556 | 2.770000 | 6.740000 | 8.570000 | 11.460000 | 390.120000 |
| 200 | 1560.000000 | 17.600192 | 8.690718 | 3.210000 | 14.155000 | 17.235000 | 20.162500 | 199.620000 |
| 201 | 1560.000000 | 7.839359 | 5.104495 | 0.000000 | 5.020000 | 6.760000 | 9.490000 | 126.530000 |
| 202 | 1560.000000 | 10.170463 | 14.622904 | 0.000000 | 6.094000 | 8.462000 | 11.953000 | 490.561000 |
| 203 | 1561.000000 | 30.073143 | 17.461798 | 7.728000 | 24.653000 | 30.097000 | 33.506000 | 500.349000 |
| 204 | 1561.000000 | 32.218169 | 565.101239 | 0.042900 | 0.114300 | 0.158200 | 0.230700 | 9998.448300 |
| 205 | 1561.000000 | 9.050122 | 11.541083 | 2.300000 | 6.040000 | 7.740000 | 9.940000 | 320.050000 |
| 206 | 1561.000000 | 0.001281 | 0.050621 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 |
| 207 | 1561.000000 | 20.376176 | 17.497556 | 4.010000 | 16.350000 | 19.720000 | 22.370000 | 457.650000 |
| 208 | 1561.000000 | 73.264316 | 28.067143 | 5.359000 | 56.158000 | 73.248000 | 90.515000 | 172.349000 |
| 209 | 1561.000000 | 0.029564 | 1.168074 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 46.150000 |
| 210 | 1543.000000 | 0.088866 | 0.042065 | 0.031900 | 0.065600 | 0.079700 | 0.099450 | 0.516400 |
| 211 | 1543.000000 | 0.056755 | 0.025005 | 0.002200 | 0.043800 | 0.053200 | 0.064200 | 0.322700 |
| 212 | 1543.000000 | 0.051432 | 0.031578 | 0.007100 | 0.032500 | 0.041600 | 0.062450 | 0.594100 |
| 213 | 1543.000000 | 0.060346 | 0.053030 | 0.003700 | 0.036400 | 0.056000 | 0.073700 | 1.283700 |
| 214 | 1543.000000 | 0.083268 | 0.056456 | 0.019300 | 0.056800 | 0.075400 | 0.093550 | 0.761500 |
| 215 | 1543.000000 | 0.081076 | 0.030437 | 0.005900 | 0.063200 | 0.082500 | 0.098300 | 0.342900 |
| 216 | 1543.000000 | 0.083484 | 0.025764 | 0.009700 | 0.069550 | 0.084600 | 0.097550 | 0.282800 |
| 217 | 1543.000000 | 0.071635 | 0.046283 | 0.007900 | 0.045800 | 0.061700 | 0.086350 | 0.674400 |
| 218 | 1566.000000 | 3.771465 | 1.170436 | 1.034000 | 2.946100 | 3.630750 | 4.404750 | 8.801500 |
| 219 | 1555.000000 | 0.003254 | 0.001646 | 0.000700 | 0.002300 | 0.003000 | 0.003800 | 0.016300 |
| 220 | 226.000000 | 0.009213 | 0.001989 | 0.005700 | 0.007800 | 0.008950 | 0.010300 | 0.024000 |
| 221 | 1567.000000 | 0.060718 | 0.023305 | 0.020000 | 0.040200 | 0.060900 | 0.076500 | 0.230500 |
| 222 | 1567.000000 | 0.008821 | 0.055937 | 0.000300 | 0.001400 | 0.002300 | 0.005500 | 0.991100 |
| 223 | 1567.000000 | 122.846571 | 55.156003 | 32.263700 | 95.147350 | 119.436000 | 144.502800 | 1768.880200 |
| 224 | 1516.000000 | 0.059370 | 0.071211 | 0.009300 | 0.029775 | 0.039800 | 0.061300 | 1.436100 |
| 225 | 1516.000000 | 1041.056588 | 433.170076 | 168.799800 | 718.725350 | 967.299800 | 1261.299800 | 3601.299800 |
| 226 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 227 | 1565.000000 | 0.019125 | 0.010756 | 0.006200 | 0.013200 | 0.016500 | 0.021200 | 0.154100 |
| 228 | 1565.000000 | 0.017844 | 0.010745 | 0.007200 | 0.012600 | 0.015500 | 0.020000 | 0.213300 |
| 229 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 230 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 231 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 232 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 233 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 234 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 235 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 236 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 237 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 238 | 1565.000000 | 0.004791 | 0.001698 | 0.001300 | 0.003700 | 0.004600 | 0.005700 | 0.024400 |
| 239 | 1565.000000 | 0.004575 | 0.001441 | 0.001400 | 0.003600 | 0.004400 | 0.005300 | 0.023600 |
| 240 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 241 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 242 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 243 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 244 | 549.000000 | 0.005755 | 0.084618 | 0.000300 | 0.001200 | 0.001700 | 0.002600 | 1.984400 |
| 245 | 549.000000 | 1.729723 | 4.335614 | 0.291400 | 0.911500 | 1.185100 | 1.761800 | 99.902200 |
| 246 | 549.000000 | 4.148742 | 10.045084 | 1.102200 | 2.725900 | 3.673000 | 4.479700 | 237.183700 |
| 247 | 852.000000 | 0.053374 | 0.066880 | 0.000000 | 0.019200 | 0.027000 | 0.051500 | 0.491400 |
| 248 | 1567.000000 | 0.025171 | 0.049235 | 0.003000 | 0.014700 | 0.021000 | 0.027300 | 0.973200 |
| 249 | 1567.000000 | 0.001065 | 0.015771 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.413800 |
| 250 | 1567.000000 | 109.650967 | 54.597274 | 21.010700 | 76.132150 | 103.093600 | 131.758400 | 1119.704200 |
| 251 | 1567.000000 | 0.004285 | 0.037472 | 0.000300 | 0.000700 | 0.001000 | 0.001300 | 0.990900 |
| 252 | 1567.000000 | 4.645115 | 64.354756 | 0.767300 | 2.205650 | 2.864600 | 3.795050 | 2549.988500 |
| 253 | 1543.000000 | 0.033216 | 0.022425 | 0.009400 | 0.024500 | 0.030800 | 0.037900 | 0.451700 |
| 254 | 1567.000000 | 0.013943 | 0.009132 | 0.001700 | 0.004700 | 0.015000 | 0.021300 | 0.078700 |
| 255 | 1567.000000 | 0.403848 | 0.120334 | 0.126900 | 0.307600 | 0.405100 | 0.480950 | 0.925500 |
| 256 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 257 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 258 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 259 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 260 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 261 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 262 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 263 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 264 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 265 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 266 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 267 | 1559.000000 | 0.070587 | 0.029649 | 0.019800 | 0.044000 | 0.070600 | 0.091650 | 0.157800 |
| 268 | 1559.000000 | 19.504677 | 7.344404 | 6.098000 | 13.828000 | 17.977000 | 24.653000 | 40.855000 |
| 269 | 1559.000000 | 3.777866 | 1.152329 | 1.301700 | 2.956500 | 3.703500 | 4.379400 | 10.152900 |
| 270 | 1562.000000 | 29.260291 | 8.402013 | 15.547100 | 24.982300 | 28.773500 | 31.702200 | 158.526000 |
| 271 | 1561.000000 | 46.056598 | 17.866438 | 10.401500 | 30.013900 | 45.676500 | 59.594700 | 132.647900 |
| 272 | 1560.000000 | 41.298147 | 17.737513 | 6.943100 | 27.092725 | 40.019250 | 54.277325 | 122.117400 |
| 273 | 1553.000000 | 20.181246 | 3.830463 | 8.651200 | 18.247100 | 19.580900 | 22.097300 | 43.573700 |
| 274 | 1553.000000 | 136.292426 | 85.607784 | 0.000000 | 81.215600 | 110.601400 | 162.038200 | 659.169600 |
| 275 | 1553.000000 | 8.693213 | 168.949413 | 0.011100 | 0.044700 | 0.078400 | 0.144900 | 3332.596400 |
| 276 | 1553.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 277 | 1553.000000 | 2.210744 | 1.196437 | 0.561500 | 1.697700 | 2.083100 | 2.514300 | 32.170900 |
| 278 | 1558.000000 | 0.001117 | 0.000340 | 0.000000 | 0.000900 | 0.001100 | 0.001300 | 0.003400 |
| 279 | 1565.000000 | 0.041057 | 0.020289 | 0.010700 | 0.028300 | 0.037200 | 0.045800 | 0.188400 |
| 280 | 1565.000000 | 0.018034 | 0.006483 | 0.007300 | 0.014200 | 0.016900 | 0.020700 | 0.075500 |
| 281 | 1565.000000 | 0.015094 | 0.005545 | 0.006900 | 0.011900 | 0.013900 | 0.016600 | 0.059700 |
| 282 | 1565.000000 | 0.005770 | 0.008550 | 0.001600 | 0.003300 | 0.005300 | 0.007100 | 0.308300 |
| 283 | 1565.000000 | 2.803984 | 5.864324 | 0.505000 | 2.210400 | 2.658000 | 3.146200 | 232.804900 |
| 284 | 1564.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 285 | 1564.000000 | 2.119795 | 0.962923 | 0.461100 | 1.438175 | 1.875150 | 2.606950 | 6.869800 |
| 286 | 1564.000000 | 4.260018 | 9.763829 | 0.728000 | 2.467200 | 3.360050 | 4.311425 | 207.016100 |
| 287 | 1564.000000 | 0.367529 | 7.386343 | 0.051300 | 0.114875 | 0.138950 | 0.198450 | 292.227400 |
| 288 | 1564.000000 | 0.003924 | 0.002936 | 0.001200 | 0.002400 | 0.003600 | 0.004900 | 0.074900 |
| 289 | 1564.000000 | 2.578596 | 1.616993 | 0.396000 | 2.092125 | 2.549000 | 3.024525 | 59.518700 |
| 290 | 1557.000000 | 0.123427 | 0.270987 | 0.041600 | 0.064900 | 0.083300 | 0.118100 | 4.420300 |
| 291 | 1567.000000 | 0.019926 | 0.025549 | 0.003800 | 0.012500 | 0.016900 | 0.023600 | 0.691500 |
| 292 | 138.000000 | 0.014487 | 0.011494 | 0.004100 | 0.008725 | 0.011000 | 0.014925 | 0.083100 |
| 293 | 138.000000 | 335.551157 | 137.692483 | 82.323300 | 229.809450 | 317.867100 | 403.989300 | 879.226000 |
| 294 | 1565.000000 | 401.814750 | 477.050076 | 0.000000 | 185.089800 | 278.671900 | 428.554500 | 3933.755000 |
| 295 | 1565.000000 | 252.999118 | 283.530702 | 0.000000 | 130.220300 | 195.825600 | 273.952600 | 2005.874400 |
| 296 | 1565.000000 | 1879.228369 | 1975.111365 | 0.000000 | 603.032900 | 1202.412100 | 2341.288700 | 15559.952500 |
| 297 | 1565.000000 | 2342.826978 | 3226.924298 | 0.000000 | 210.936600 | 820.098800 | 3190.616400 | 18520.468300 |
| 298 | 1565.000000 | 0.063804 | 0.064225 | 0.000000 | 0.040700 | 0.052800 | 0.069200 | 0.526400 |
| 299 | 1565.000000 | 0.060267 | 0.130825 | 0.000000 | 0.030200 | 0.040000 | 0.052000 | 1.031200 |
| 300 | 1565.000000 | 0.118386 | 0.219147 | 0.000000 | 0.058900 | 0.082800 | 0.115500 | 1.812300 |
| 301 | 1565.000000 | 0.910146 | 0.331982 | 0.310000 | 0.717200 | 0.860400 | 1.046400 | 5.711000 |
| 302 | 1565.000000 | 0.403342 | 0.197514 | 0.111800 | 0.295800 | 0.380800 | 0.477000 | 5.154900 |
| 303 | 1565.000000 | 0.040344 | 0.014511 | 0.010800 | 0.030000 | 0.038800 | 0.048600 | 0.225800 |
| 304 | 1565.000000 | 0.132076 | 0.064867 | 0.013800 | 0.072800 | 0.137200 | 0.178500 | 0.333700 |
| 305 | 1566.000000 | 0.264917 | 0.057387 | 0.117100 | 0.225000 | 0.264300 | 0.307500 | 0.475000 |
| 306 | 1566.000000 | 0.048623 | 0.025400 | 0.003400 | 0.033100 | 0.044800 | 0.055200 | 0.224600 |
| 307 | 1566.000000 | 0.128921 | 0.027468 | 0.054900 | 0.113700 | 0.129500 | 0.147600 | 0.211200 |
| 308 | 1566.000000 | 0.218414 | 0.033593 | 0.091300 | 0.197600 | 0.219450 | 0.237900 | 0.323900 |
| 309 | 1566.000000 | 0.128921 | 0.027470 | 0.054900 | 0.113700 | 0.129500 | 0.147600 | 0.211200 |
| 310 | 1566.000000 | 0.304752 | 0.043460 | 0.180900 | 0.278550 | 0.302900 | 0.331900 | 0.443800 |
| 311 | 1566.000000 | 0.097344 | 0.028796 | 0.032800 | 0.077600 | 0.097700 | 0.115900 | 0.178400 |
| 312 | 1566.000000 | 0.160051 | 0.117316 | 0.022400 | 0.091500 | 0.121500 | 0.160175 | 0.754900 |
| 313 | 1543.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 314 | 1543.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 315 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 316 | 1566.000000 | 5.976977 | 1.018629 | 2.788200 | 5.301525 | 5.831500 | 6.547800 | 13.095800 |
| 317 | 1566.000000 | 0.172629 | 0.072392 | 0.028300 | 0.117375 | 0.163400 | 0.218100 | 1.003400 |
| 318 | 1566.000000 | 3.188770 | 1.215930 | 0.984800 | 2.319725 | 2.898900 | 4.021250 | 15.893400 |
| 319 | 1566.000000 | 7.916036 | 2.179059 | 1.657400 | 6.245150 | 8.388800 | 9.481100 | 20.045500 |
| 320 | 1566.000000 | 0.043105 | 0.031885 | 0.008400 | 0.031200 | 0.039850 | 0.050200 | 0.947400 |
| 321 | 1566.000000 | 2.263727 | 2.116994 | 0.611400 | 1.670075 | 2.077650 | 2.633350 | 79.151500 |
| 322 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 323 | 1566.000000 | 5.393420 | 2.518859 | 1.710100 | 4.272950 | 5.458800 | 6.344875 | 89.191700 |
| 324 | 1566.000000 | 13.332172 | 6.615850 | 2.234500 | 7.578600 | 12.504500 | 17.925175 | 51.867800 |
| 325 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 326 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 327 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 328 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 329 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 330 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 331 | 1563.000000 | 0.083232 | 0.063425 | 0.022400 | 0.068800 | 0.084800 | 0.095600 | 1.095900 |
| 332 | 1560.000000 | 2.593485 | 5.645226 | 0.537300 | 1.546550 | 2.062700 | 2.790525 | 174.894400 |
| 333 | 1561.000000 | 6.215866 | 3.403447 | 2.837200 | 5.453900 | 5.980100 | 6.549500 | 90.515900 |
| 334 | 1561.000000 | 0.168364 | 0.172883 | 0.028200 | 0.089400 | 0.129400 | 0.210400 | 3.412500 |
| 335 | 1561.000000 | 3.426925 | 5.781558 | 0.789900 | 2.035700 | 2.513500 | 3.360400 | 172.711900 |
| 336 | 1560.000000 | 9.736386 | 7.556131 | 5.215100 | 8.288525 | 9.073550 | 10.041625 | 214.862800 |
| 337 | 1560.000000 | 2.327482 | 1.699435 | 0.000000 | 1.542850 | 2.054450 | 2.785475 | 38.899500 |
| 338 | 1560.000000 | 3.037580 | 5.645022 | 0.000000 | 1.901350 | 2.560850 | 3.405450 | 196.688000 |
| 339 | 1561.000000 | 9.328958 | 6.074702 | 2.200100 | 7.588900 | 9.474200 | 10.439900 | 197.498800 |
| 340 | 1561.000000 | 14.673507 | 261.738451 | 0.013100 | 0.034600 | 0.046400 | 0.066800 | 5043.878900 |
| 341 | 1561.000000 | 2.732094 | 3.667902 | 0.574100 | 1.911800 | 2.377300 | 2.985400 | 97.708900 |
| 342 | 1561.000000 | 0.000286 | 0.011319 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.447200 |
| 343 | 1561.000000 | 6.198508 | 5.371825 | 1.256500 | 4.998900 | 6.005600 | 6.885200 | 156.336000 |
| 344 | 1561.000000 | 23.217146 | 8.895221 | 2.056000 | 17.860900 | 23.214700 | 28.873100 | 59.324100 |
| 345 | 773.000000 | 7.958376 | 17.512965 | 1.769400 | 4.440600 | 5.567000 | 6.825500 | 257.010600 |
| 346 | 773.000000 | 5.770212 | 17.077498 | 1.017700 | 2.532700 | 3.046400 | 4.085700 | 187.758900 |
| 347 | 1561.000000 | 0.008914 | 0.352186 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 13.914700 |
| 348 | 1543.000000 | 0.024706 | 0.011862 | 0.010300 | 0.018000 | 0.022600 | 0.027300 | 0.220000 |
| 349 | 1543.000000 | 0.025252 | 0.010603 | 0.001000 | 0.019600 | 0.024000 | 0.028600 | 0.133900 |
| 350 | 1543.000000 | 0.023202 | 0.014326 | 0.002900 | 0.014600 | 0.018800 | 0.028500 | 0.291400 |
| 351 | 1543.000000 | 0.027584 | 0.024563 | 0.002000 | 0.016600 | 0.025300 | 0.033900 | 0.618800 |
| 352 | 1543.000000 | 0.023356 | 0.013157 | 0.005600 | 0.016000 | 0.022000 | 0.026900 | 0.142900 |
| 353 | 1543.000000 | 0.040331 | 0.015499 | 0.002600 | 0.030200 | 0.042100 | 0.050200 | 0.153500 |
| 354 | 1543.000000 | 0.041921 | 0.013068 | 0.004000 | 0.034850 | 0.044200 | 0.050000 | 0.134400 |
| 355 | 1543.000000 | 0.034543 | 0.022307 | 0.003800 | 0.021200 | 0.029400 | 0.042300 | 0.278900 |
| 356 | 1566.000000 | 1.298629 | 0.386918 | 0.379600 | 1.025475 | 1.255300 | 1.533325 | 2.834800 |
| 357 | 1555.000000 | 0.000999 | 0.000501 | 0.000300 | 0.000700 | 0.000900 | 0.001100 | 0.005200 |
| 358 | 226.000000 | 0.002443 | 0.000395 | 0.001700 | 0.002200 | 0.002400 | 0.002700 | 0.004700 |
| 359 | 1567.000000 | 0.019840 | 0.007136 | 0.007600 | 0.013800 | 0.019600 | 0.025000 | 0.088800 |
| 360 | 1567.000000 | 0.002945 | 0.020003 | 0.000100 | 0.000400 | 0.000700 | 0.001800 | 0.409000 |
| 361 | 1567.000000 | 39.936406 | 17.056304 | 10.720400 | 32.168700 | 39.696100 | 47.079200 | 547.172200 |
| 362 | 1516.000000 | 0.018383 | 0.021644 | 0.002800 | 0.009500 | 0.012500 | 0.018600 | 0.416300 |
| 363 | 1516.000000 | 333.319601 | 138.801928 | 60.988200 | 228.682525 | 309.831650 | 412.329775 | 1072.203100 |
| 364 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 365 | 1565.000000 | 0.005199 | 0.002656 | 0.001700 | 0.003800 | 0.004600 | 0.005800 | 0.036800 |
| 366 | 1565.000000 | 0.004814 | 0.002382 | 0.002000 | 0.003500 | 0.004300 | 0.005400 | 0.039200 |
| 367 | 1561.000000 | 0.003773 | 0.002699 | 0.000000 | 0.002600 | 0.003200 | 0.004200 | 0.035700 |
| 368 | 1561.000000 | 0.003172 | 0.002107 | 0.000000 | 0.002200 | 0.002800 | 0.003600 | 0.033400 |
| 369 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 370 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 371 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 372 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 373 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 374 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 375 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 376 | 1565.000000 | 0.001601 | 0.000534 | 0.000400 | 0.001300 | 0.001600 | 0.001900 | 0.008200 |
| 377 | 1565.000000 | 0.001571 | 0.000467 | 0.000400 | 0.001300 | 0.001500 | 0.001800 | 0.007700 |
| 378 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 379 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 380 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 381 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 382 | 549.000000 | 0.001826 | 0.026740 | 0.000100 | 0.000400 | 0.000500 | 0.000800 | 0.627100 |
| 383 | 549.000000 | 0.541040 | 1.341020 | 0.087500 | 0.295500 | 0.372600 | 0.541200 | 30.998200 |
| 384 | 549.000000 | 1.285448 | 3.168427 | 0.338300 | 0.842300 | 1.106300 | 1.386600 | 74.844500 |
| 385 | 852.000000 | 0.011427 | 0.014366 | 0.000000 | 0.005300 | 0.006800 | 0.011325 | 0.207300 |
| 386 | 1567.000000 | 0.008281 | 0.015488 | 0.000800 | 0.004800 | 0.006800 | 0.009300 | 0.306800 |
| 387 | 1567.000000 | 0.000339 | 0.004989 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.130900 |
| 388 | 1567.000000 | 35.155091 | 17.227003 | 6.310100 | 24.386550 | 32.530700 | 42.652450 | 348.829300 |
| 389 | 1567.000000 | 0.001338 | 0.011816 | 0.000100 | 0.000200 | 0.000300 | 0.000400 | 0.312700 |
| 390 | 1567.000000 | 1.431868 | 20.326415 | 0.304600 | 0.675150 | 0.877300 | 1.148200 | 805.393600 |
| 391 | 1543.000000 | 0.010956 | 0.006738 | 0.003100 | 0.008300 | 0.010200 | 0.012400 | 0.137500 |
| 392 | 1567.000000 | 0.004533 | 0.002956 | 0.000500 | 0.001500 | 0.004900 | 0.006900 | 0.022900 |
| 393 | 1567.000000 | 0.133990 | 0.038408 | 0.034200 | 0.104400 | 0.133900 | 0.160400 | 0.299400 |
| 394 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 395 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 396 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 397 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 398 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 399 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 400 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 401 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 402 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 403 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 404 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 405 | 1559.000000 | 0.024208 | 0.010728 | 0.006200 | 0.014000 | 0.023900 | 0.032300 | 0.051400 |
| 406 | 1559.000000 | 6.730615 | 2.829583 | 2.054500 | 4.547600 | 5.920100 | 8.585200 | 14.727700 |
| 407 | 1559.000000 | 1.231997 | 0.364711 | 0.424000 | 0.966500 | 1.239700 | 1.416700 | 3.312800 |
| 408 | 1562.000000 | 5.340932 | 2.578118 | 2.737800 | 4.127800 | 4.922450 | 5.787100 | 44.310000 |
| 409 | 1561.000000 | 4.580430 | 1.776843 | 1.216300 | 3.012800 | 4.489700 | 5.936700 | 9.576500 |
| 410 | 1560.000000 | 4.929344 | 2.122978 | 0.734200 | 3.265075 | 4.732750 | 6.458300 | 13.807100 |
| 411 | 1553.000000 | 2.616086 | 0.551474 | 0.960900 | 2.321300 | 2.548100 | 2.853200 | 6.215000 |
| 412 | 1553.000000 | 30.911316 | 18.413622 | 0.000000 | 18.407900 | 26.156900 | 38.139700 | 128.281600 |
| 413 | 1553.000000 | 25.612690 | 47.308463 | 4.041600 | 11.375800 | 20.255100 | 29.307300 | 899.119000 |
| 414 | 1553.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 415 | 1553.000000 | 6.630616 | 3.958371 | 1.534000 | 4.927400 | 6.176600 | 7.570700 | 116.861500 |
| 416 | 1558.000000 | 3.404349 | 1.035433 | 0.000000 | 2.660100 | 3.234000 | 4.010700 | 9.690000 |
| 417 | 1565.000000 | 8.190905 | 4.054515 | 2.153100 | 5.765500 | 7.395600 | 9.168800 | 39.037600 |
| 418 | 1565.000000 | 320.259235 | 287.704482 | 0.000000 | 0.000000 | 302.177600 | 524.002200 | 999.316000 |
| 419 | 1565.000000 | 309.061299 | 325.448391 | 0.000000 | 0.000000 | 272.448700 | 582.935200 | 998.681300 |
| 420 | 1565.000000 | 1.821261 | 3.057692 | 0.441100 | 1.030400 | 1.645100 | 2.214700 | 111.495600 |
| 421 | 1565.000000 | 4.174524 | 6.913855 | 0.721700 | 3.184200 | 3.943100 | 4.784300 | 273.095200 |
| 422 | 1564.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 423 | 1564.000000 | 77.660446 | 32.596933 | 23.020000 | 55.976675 | 69.905450 | 92.911500 | 424.215200 |
| 424 | 1564.000000 | 3.315469 | 6.325365 | 0.486600 | 1.965250 | 2.667100 | 3.470975 | 103.180900 |
| 425 | 1564.000000 | 6.796312 | 23.257716 | 1.466600 | 3.766200 | 4.764400 | 6.883500 | 898.608500 |
| 426 | 1564.000000 | 1.233858 | 0.995620 | 0.363200 | 0.743425 | 1.135300 | 1.539500 | 24.990400 |
| 427 | 1564.000000 | 4.058501 | 3.042144 | 0.663700 | 3.113225 | 3.941450 | 4.768650 | 113.223000 |
| 428 | 1557.000000 | 4.220747 | 10.632730 | 1.119800 | 1.935500 | 2.534100 | 3.609000 | 118.753300 |
| 429 | 1567.000000 | 4.171844 | 6.435390 | 0.783700 | 2.571400 | 3.453800 | 4.755800 | 186.616400 |
| 430 | 1565.000000 | 18.421600 | 36.060084 | 0.000000 | 6.999700 | 11.105600 | 17.423100 | 400.000000 |
| 431 | 1565.000000 | 22.358305 | 36.395408 | 0.000000 | 11.059000 | 16.381000 | 21.765200 | 400.000000 |
| 432 | 1565.000000 | 99.367633 | 126.188715 | 0.000000 | 31.032400 | 57.969300 | 120.172900 | 994.285700 |
| 433 | 1565.000000 | 205.519304 | 225.778870 | 0.000000 | 10.027100 | 151.115600 | 305.026300 | 995.744700 |
| 434 | 1565.000000 | 14.733945 | 34.108854 | 0.000000 | 7.550700 | 10.197700 | 12.754200 | 400.000000 |
| 435 | 1565.000000 | 9.370666 | 34.369789 | 0.000000 | 3.494400 | 4.551100 | 5.822800 | 400.000000 |
| 436 | 1565.000000 | 7.513266 | 34.557804 | 0.000000 | 1.950900 | 2.764300 | 3.822200 | 400.000000 |
| 437 | 1565.000000 | 4.016785 | 1.611274 | 1.156800 | 3.070700 | 3.780900 | 4.678600 | 32.274000 |
| 438 | 1565.000000 | 54.701052 | 34.108051 | 0.000000 | 36.290300 | 49.090900 | 66.666700 | 851.612900 |
| 439 | 1565.000000 | 70.643942 | 38.376178 | 14.120600 | 48.173800 | 65.437800 | 84.973400 | 657.762100 |
| 440 | 1565.000000 | 11.526617 | 6.169471 | 1.097300 | 5.414100 | 12.085900 | 15.796400 | 33.058000 |
| 441 | 1566.000000 | 0.802081 | 0.184213 | 0.351200 | 0.679600 | 0.807600 | 0.927600 | 1.277100 |
| 442 | 1566.000000 | 1.345259 | 0.659195 | 0.097400 | 0.907650 | 1.264550 | 1.577825 | 5.131700 |
| 443 | 1566.000000 | 0.633941 | 0.143552 | 0.216900 | 0.550500 | 0.643500 | 0.733425 | 1.085100 |
| 444 | 1566.000000 | 0.895043 | 0.155522 | 0.333600 | 0.804800 | 0.902700 | 0.988800 | 1.351100 |
| 445 | 1566.000000 | 0.647090 | 0.141252 | 0.308600 | 0.555800 | 0.651100 | 0.748400 | 1.108700 |
| 446 | 1566.000000 | 1.175003 | 0.176158 | 0.696800 | 1.046800 | 1.163800 | 1.272300 | 1.763900 |
| 447 | 1566.000000 | 0.281895 | 0.086461 | 0.084600 | 0.226100 | 0.279700 | 0.338825 | 0.508500 |
| 448 | 1566.000000 | 0.332270 | 0.236275 | 0.039900 | 0.187700 | 0.251200 | 0.351100 | 1.475400 |
| 449 | 1543.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 450 | 1543.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 451 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 452 | 1566.000000 | 5.346816 | 0.919196 | 2.670900 | 4.764200 | 5.271450 | 5.913000 | 13.977600 |
| 453 | 1566.000000 | 5.460971 | 2.250804 | 0.903700 | 3.747875 | 5.227100 | 6.902475 | 34.490200 |
| 454 | 1566.000000 | 7.883742 | 3.059660 | 2.329400 | 5.806525 | 7.424900 | 9.576775 | 42.070300 |
| 455 | 1566.000000 | 3.636633 | 0.938372 | 0.694800 | 2.899675 | 3.724500 | 4.341925 | 10.184000 |
| 456 | 1566.000000 | 12.325685 | 8.125876 | 3.048900 | 8.816575 | 11.350900 | 14.387900 | 232.125800 |
| 457 | 1566.000000 | 5.263666 | 4.537737 | 1.442800 | 3.827525 | 4.793350 | 6.089450 | 164.109300 |
| 458 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 459 | 1566.000000 | 2.838380 | 1.345576 | 0.991000 | 2.291175 | 2.830350 | 3.309225 | 47.777200 |
| 460 | 1566.000000 | 29.197414 | 13.335189 | 7.953400 | 20.221850 | 26.167850 | 35.278800 | 149.385100 |
| 461 | 1566.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 462 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 463 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 464 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 465 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 466 | 1563.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 467 | 1563.000000 | 6.252091 | 8.673724 | 1.716300 | 4.697500 | 5.645000 | 6.386900 | 109.007400 |
| 468 | 1560.000000 | 224.173047 | 230.766915 | 0.000000 | 38.472775 | 150.340100 | 335.922400 | 999.877000 |
| 469 | 1561.000000 | 5.662293 | 3.151685 | 2.600900 | 4.847200 | 5.472400 | 6.005700 | 77.800700 |
| 470 | 1561.000000 | 5.367752 | 4.983367 | 0.832500 | 2.823300 | 4.061100 | 7.006800 | 87.134700 |
| 471 | 1561.000000 | 9.638797 | 10.174117 | 2.402600 | 5.807300 | 7.396000 | 9.720200 | 212.655700 |
| 472 | 1560.000000 | 137.888406 | 47.698041 | 11.499700 | 105.525150 | 138.255150 | 168.410125 | 492.771800 |
| 473 | 1560.000000 | 39.426847 | 22.457104 | 0.000000 | 24.900800 | 34.246750 | 47.727850 | 358.950400 |
| 474 | 1560.000000 | 37.637050 | 24.822918 | 0.000000 | 23.156500 | 32.820050 | 45.169475 | 415.435500 |
| 475 | 1561.000000 | 4.262573 | 2.611174 | 1.101100 | 3.494500 | 4.276200 | 4.741800 | 79.116200 |
| 476 | 1561.000000 | 20.132155 | 14.939590 | 0.000000 | 11.577100 | 15.973800 | 23.737200 | 274.887100 |
| 477 | 1561.000000 | 6.257921 | 10.185026 | 1.687200 | 4.105400 | 5.242200 | 6.703800 | 289.826400 |
| 478 | 1561.000000 | 0.128123 | 5.062075 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 200.000000 |
| 479 | 1561.000000 | 3.283394 | 2.638608 | 0.645900 | 2.627700 | 3.184500 | 3.625300 | 63.333600 |
| 480 | 1561.000000 | 75.538131 | 35.752493 | 8.840600 | 52.894500 | 70.434500 | 93.119600 | 221.974700 |
| 481 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 482 | 1543.000000 | 318.418448 | 281.011323 | 0.000000 | 0.000000 | 293.518500 | 514.585900 | 999.413500 |
| 483 | 1543.000000 | 206.564196 | 192.864413 | 0.000000 | 81.316150 | 148.317500 | 262.865250 | 989.473700 |
| 484 | 1543.000000 | 215.288948 | 213.126638 | 0.000000 | 76.455400 | 138.775500 | 294.667050 | 996.858600 |
| 485 | 1543.000000 | 201.111728 | 218.690015 | 0.000000 | 50.383550 | 112.953400 | 288.893450 | 994.000000 |
| 486 | 1543.000000 | 302.506186 | 287.364070 | 0.000000 | 0.000000 | 249.927000 | 501.607450 | 999.491100 |
| 487 | 1543.000000 | 239.455326 | 263.837645 | 0.000000 | 55.555150 | 112.275500 | 397.506100 | 995.744700 |
| 488 | 1543.000000 | 352.616477 | 252.043751 | 0.000000 | 139.914350 | 348.529400 | 510.647150 | 997.518600 |
| 489 | 1543.000000 | 272.169707 | 228.046702 | 0.000000 | 112.859250 | 219.487200 | 377.144200 | 994.003500 |
| 490 | 1566.000000 | 51.354045 | 18.048612 | 13.722500 | 38.391100 | 48.557450 | 61.494725 | 142.843600 |
| 491 | 1555.000000 | 2.442673 | 1.224283 | 0.555800 | 1.747100 | 2.250800 | 2.839800 | 12.769800 |
| 492 | 226.000000 | 8.170943 | 1.759262 | 4.888200 | 6.924650 | 8.008950 | 9.078900 | 21.044300 |
| 493 | 1567.000000 | 2.530046 | 0.973948 | 0.833000 | 1.663750 | 2.529100 | 3.199100 | 9.402400 |
| 494 | 1567.000000 | 0.956442 | 6.615200 | 0.034200 | 0.139000 | 0.232500 | 0.563000 | 127.572800 |
| 495 | 1567.000000 | 6.807826 | 3.260019 | 1.772000 | 5.274600 | 6.607900 | 7.897200 | 107.692600 |
| 496 | 1516.000000 | 29.865896 | 24.621586 | 4.813500 | 16.342300 | 22.039100 | 32.438475 | 219.643600 |
| 497 | 1516.000000 | 11.821030 | 4.956647 | 1.949600 | 8.150350 | 10.906550 | 14.469050 | 40.281800 |
| 498 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 499 | 1565.000000 | 263.195864 | 324.771342 | 0.000000 | 0.000000 | 0.000000 | 536.204600 | 1000.000000 |
| 500 | 1565.000000 | 240.981377 | 323.003410 | 0.000000 | 0.000000 | 0.000000 | 505.401000 | 999.233700 |
| 501 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 502 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 503 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 504 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 505 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 506 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 507 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 508 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 509 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 510 | 1565.000000 | 55.763508 | 37.691736 | 0.000000 | 35.322200 | 46.986100 | 64.248700 | 451.485100 |
| 511 | 1565.000000 | 275.979457 | 329.664680 | 0.000000 | 0.000000 | 0.000000 | 555.294100 | 1000.000000 |
| 512 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 513 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 514 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 515 | 1561.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 516 | 549.000000 | 0.678898 | 10.783880 | 0.028700 | 0.121500 | 0.174700 | 0.264900 | 252.860400 |
| 517 | 549.000000 | 1.738902 | 4.890663 | 0.288000 | 0.890300 | 1.154300 | 1.759700 | 113.275800 |
| 518 | 549.000000 | 1.806273 | 4.715894 | 0.467400 | 1.171200 | 1.589100 | 1.932800 | 111.349500 |
| 519 | 852.000000 | 11.728440 | 15.814420 | 0.000000 | 4.160300 | 5.832950 | 10.971850 | 184.348800 |
| 520 | 1567.000000 | 2.695999 | 5.702366 | 0.312100 | 1.552150 | 2.221000 | 2.903700 | 111.736500 |
| 521 | 1567.000000 | 11.610080 | 103.122996 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1000.000000 |
| 522 | 1567.000000 | 14.728866 | 7.104435 | 2.681100 | 10.182800 | 13.742600 | 17.808950 | 137.983800 |
| 523 | 1567.000000 | 0.453896 | 4.147581 | 0.025800 | 0.073050 | 0.100000 | 0.133200 | 111.333000 |
| 524 | 1567.000000 | 5.687782 | 20.663414 | 1.310400 | 3.769650 | 4.877100 | 6.450650 | 818.000500 |
| 525 | 1543.000000 | 5.560397 | 3.920370 | 1.540000 | 4.101500 | 5.134200 | 6.329500 | 80.040600 |
| 526 | 1567.000000 | 1.443457 | 0.958428 | 0.170500 | 0.484200 | 1.550100 | 2.211650 | 8.203700 |
| 527 | 1567.000000 | 6.395717 | 1.888698 | 2.170000 | 4.895450 | 6.410800 | 7.594250 | 14.447900 |
| 528 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 529 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 530 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 531 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 532 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 533 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 534 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 535 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 536 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 537 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 538 | 1558.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 539 | 1559.000000 | 3.034235 | 1.252913 | 0.851600 | 1.889900 | 3.054800 | 3.947000 | 6.580300 |
| 540 | 1559.000000 | 1.942828 | 0.731928 | 0.614400 | 1.385300 | 1.785500 | 2.458350 | 4.082500 |
| 541 | 1559.000000 | 9.611628 | 2.896376 | 3.276100 | 7.495750 | 9.459300 | 11.238400 | 25.779200 |
| 542 | 1565.000000 | 0.111208 | 0.002737 | 0.105300 | 0.109600 | 0.109600 | 0.113400 | 0.118400 |
| 543 | 1565.000000 | 0.008471 | 0.001534 | 0.005100 | 0.007800 | 0.007800 | 0.009000 | 0.024000 |
| 544 | 1565.000000 | 0.002509 | 0.000296 | 0.001600 | 0.002400 | 0.002600 | 0.002600 | 0.004700 |
| 545 | 1565.000000 | 7.611403 | 1.315544 | 4.429400 | 7.116000 | 7.116000 | 8.020700 | 21.044300 |
| 546 | 1307.000000 | 1.039630 | 0.389066 | 0.444400 | 0.797500 | 0.911100 | 1.285550 | 3.978600 |
| 547 | 1307.000000 | 403.546477 | 5.063887 | 372.822000 | 400.694000 | 403.122000 | 407.431000 | 421.702000 |
| 548 | 1307.000000 | 75.679871 | 3.390523 | 71.038000 | 73.254000 | 74.084000 | 78.397000 | 83.720000 |
| 549 | 1307.000000 | 0.663256 | 0.673346 | 0.044600 | 0.226250 | 0.471000 | 0.850350 | 7.065600 |
| 550 | 1307.000000 | 17.013313 | 4.966954 | 6.110000 | 14.530000 | 16.340000 | 19.035000 | 131.680000 |
| 551 | 1307.000000 | 1.230712 | 1.361117 | 0.120000 | 0.870000 | 1.150000 | 1.370000 | 39.330000 |
| 552 | 1307.000000 | 0.276688 | 0.276231 | 0.018700 | 0.094900 | 0.197900 | 0.358450 | 2.718200 |
| 553 | 1307.000000 | 7.703874 | 2.192647 | 2.786000 | 6.738100 | 7.427900 | 8.637150 | 56.930300 |
| 554 | 1307.000000 | 0.503657 | 0.598852 | 0.052000 | 0.343800 | 0.478900 | 0.562350 | 17.478100 |
| 555 | 1307.000000 | 57.746537 | 35.207552 | 4.826900 | 27.017600 | 54.441700 | 74.628700 | 303.550000 |
| 556 | 1307.000000 | 4.216905 | 1.280008 | 1.496700 | 3.625100 | 4.067100 | 4.702700 | 35.319800 |
| 557 | 1307.000000 | 1.623070 | 1.870433 | 0.164600 | 1.182900 | 1.529800 | 1.815600 | 54.291700 |
| 558 | 1566.000000 | 0.995009 | 0.083860 | 0.891900 | 0.955200 | 0.972700 | 1.000800 | 1.512100 |
| 559 | 1566.000000 | 0.325708 | 0.201392 | 0.069900 | 0.149825 | 0.290900 | 0.443600 | 1.073700 |
| 560 | 1566.000000 | 0.072443 | 0.051578 | 0.017700 | 0.036200 | 0.059200 | 0.089000 | 0.445700 |
| 561 | 1566.000000 | 32.284956 | 19.026081 | 7.236900 | 15.762450 | 29.731150 | 44.113400 | 101.114600 |
| 562 | 1294.000000 | 262.729683 | 7.630585 | 242.286000 | 259.972500 | 264.272000 | 265.707000 | 311.404000 |
| 563 | 1294.000000 | 0.679641 | 0.121758 | 0.304900 | 0.567100 | 0.651000 | 0.768875 | 1.298800 |
| 564 | 1294.000000 | 6.444985 | 2.633583 | 0.970000 | 4.980000 | 5.160000 | 7.800000 | 32.580000 |
| 565 | 1294.000000 | 0.145610 | 0.081122 | 0.022400 | 0.087700 | 0.119550 | 0.186150 | 0.689200 |
| 566 | 1294.000000 | 2.610870 | 1.032761 | 0.412200 | 2.090200 | 2.150450 | 3.098725 | 14.014100 |
| 567 | 1294.000000 | 0.060086 | 0.032761 | 0.009100 | 0.038200 | 0.048650 | 0.075275 | 0.293200 |
| 568 | 1294.000000 | 2.452417 | 0.996644 | 0.370600 | 1.884400 | 1.999700 | 2.970850 | 12.746200 |
| 569 | 1294.000000 | 21.117674 | 10.213294 | 3.250400 | 15.466200 | 16.988350 | 24.772175 | 84.802400 |
| 570 | 1567.000000 | 530.523623 | 17.499736 | 317.196400 | 530.702700 | 532.398200 | 534.356400 | 589.508200 |
| 571 | 1567.000000 | 2.101836 | 0.275112 | 0.980200 | 1.982900 | 2.118600 | 2.290650 | 2.739500 |
| 572 | 1567.000000 | 28.450165 | 86.304681 | 3.540000 | 7.500000 | 8.650000 | 10.130000 | 454.560000 |
| 573 | 1567.000000 | 0.345636 | 0.248478 | 0.066700 | 0.242250 | 0.293400 | 0.366900 | 2.196700 |
| 574 | 1567.000000 | 9.162315 | 26.920150 | 1.039500 | 2.567850 | 2.975800 | 3.492500 | 170.020400 |
| 575 | 1567.000000 | 0.104729 | 0.067791 | 0.023000 | 0.075100 | 0.089500 | 0.112150 | 0.550200 |
| 576 | 1567.000000 | 5.563747 | 16.921369 | 0.663600 | 1.408450 | 1.624500 | 1.902000 | 90.423500 |
| 577 | 1567.000000 | 16.642363 | 12.485267 | 4.582000 | 11.501550 | 13.817900 | 17.080900 | 96.960100 |
| 578 | 618.000000 | 0.021615 | 0.011730 | -0.016900 | 0.013800 | 0.020400 | 0.027700 | 0.102800 |
| 579 | 618.000000 | 0.016829 | 0.009640 | 0.003200 | 0.010600 | 0.014800 | 0.020000 | 0.079900 |
| 580 | 618.000000 | 0.005396 | 0.003116 | 0.001000 | 0.003400 | 0.004700 | 0.006475 | 0.028600 |
| 581 | 618.000000 | 97.934373 | 87.520966 | 0.000000 | 46.184900 | 72.288900 | 116.539150 | 737.304800 |
| 582 | 1566.000000 | 0.500096 | 0.003404 | 0.477800 | 0.497900 | 0.500200 | 0.502375 | 0.509800 |
| 583 | 1566.000000 | 0.015318 | 0.017180 | 0.006000 | 0.011600 | 0.013800 | 0.016500 | 0.476600 |
| 584 | 1566.000000 | 0.003847 | 0.003720 | 0.001700 | 0.003100 | 0.003600 | 0.004100 | 0.104500 |
| 585 | 1566.000000 | 3.067826 | 3.578033 | 1.197500 | 2.306500 | 2.757650 | 3.295175 | 99.303200 |
| 586 | 1566.000000 | 0.021458 | 0.012358 | -0.016900 | 0.013425 | 0.020500 | 0.027600 | 0.102800 |
| 587 | 1566.000000 | 0.016475 | 0.008808 | 0.003200 | 0.010600 | 0.014800 | 0.020300 | 0.079900 |
| 588 | 1566.000000 | 0.005283 | 0.002867 | 0.001000 | 0.003300 | 0.004600 | 0.006400 | 0.028600 |
| 589 | 1566.000000 | 99.670066 | 93.891919 | 0.000000 | 44.368600 | 71.900500 | 114.749700 | 737.304800 |
| Pass/Fail | 1567.000000 | -0.867262 | 0.498010 | -1.000000 | -1.000000 | -1.000000 | -1.000000 | 1.000000 |
signal_df.describe().style
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1561.000000 | 1560.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1558.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1557.000000 | 1567.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1560.000000 | 1560.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 773.000000 | 773.000000 | 1561.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1555.000000 | 226.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1516.000000 | 1516.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 549.000000 | 549.000000 | 549.000000 | 852.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1543.000000 | 1567.000000 | 1567.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1559.000000 | 1559.000000 | 1559.000000 | 1562.000000 | 1561.000000 | 1560.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1558.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1557.000000 | 1567.000000 | 138.000000 | 138.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1543.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1560.000000 | 1560.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1555.000000 | 226.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1516.000000 | 1516.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 549.000000 | 549.000000 | 549.000000 | 852.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1543.000000 | 1567.000000 | 1567.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1559.000000 | 1559.000000 | 1559.000000 | 1562.000000 | 1561.000000 | 1560.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1558.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1557.000000 | 1567.000000 | 138.000000 | 138.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1560.000000 | 1560.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 773.000000 | 773.000000 | 1561.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1555.000000 | 226.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1516.000000 | 1516.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 549.000000 | 549.000000 | 549.000000 | 852.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1543.000000 | 1567.000000 | 1567.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1559.000000 | 1559.000000 | 1559.000000 | 1562.000000 | 1561.000000 | 1560.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1553.000000 | 1558.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1564.000000 | 1557.000000 | 1567.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1563.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1560.000000 | 1560.000000 | 1560.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1543.000000 | 1566.000000 | 1555.000000 | 226.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1516.000000 | 1516.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1565.000000 | 1565.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 1561.000000 | 549.000000 | 549.000000 | 549.000000 | 852.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1543.000000 | 1567.000000 | 1567.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1558.000000 | 1559.000000 | 1559.000000 | 1559.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1565.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1307.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1294.000000 | 1294.000000 | 1294.000000 | 1294.000000 | 1294.000000 | 1294.000000 | 1294.000000 | 1294.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 1567.000000 | 618.000000 | 618.000000 | 618.000000 | 618.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1566.000000 | 1567.000000 |
| mean | 3014.452896 | 2495.850231 | 2200.547318 | 1396.376627 | 4.197013 | 100.000000 | 101.112908 | 0.121822 | 1.462862 | -0.000841 | 0.000146 | 0.964353 | 199.956809 | 0.000000 | 9.005371 | 413.086035 | 9.907603 | 0.971444 | 190.047354 | 12.481034 | 1.405054 | -5618.393610 | 2699.378435 | -3806.299734 | -298.598136 | 1.203845 | 1.938477 | 6.638628 | 69.499532 | 2.366197 | 0.184159 | 3.673189 | 85.337469 | 8.960279 | 50.582639 | 64.555787 | 49.417370 | 66.221274 | 86.836577 | 118.679554 | 67.904909 | 3.353066 | 70.000000 | 355.538904 | 10.031165 | 136.743060 | 733.672811 | 1.177958 | 139.972231 | 1.000000 | 632.254197 | 157.420991 | 0.000000 | 4.592971 | 4.838523 | 2856.172105 | 0.928849 | 0.949215 | 4.593312 | 2.960241 | 355.159094 | 10.423143 | 116.502329 | 13.989927 | 20.542109 | 27.131816 | 706.668523 | 16.715444 | 147.437578 | 1.000000 | 619.101687 | 104.329033 | 150.361552 | 468.020404 | 0.002688 | -0.006903 | -0.029390 | -0.007041 | -0.013643 | 0.003458 | -0.018531 | -0.021153 | 0.006055 | 7.452067 | 0.133108 | 0.112783 | 2.401872 | 0.982420 | 1807.815021 | 0.188703 | 8827.536865 | 0.002440 | 0.000507 | -0.000541 | -0.000029 | 0.000060 | 0.017127 | 0.000000 | -0.018143 | 0.001540 | -0.000021 | -0.000007 | 0.001115 | -0.009789 | -0.000015 | -0.000498 | 0.000540 | -0.001766 | -0.010789 | 0.979993 | 101.318253 | 231.818898 | 0.457538 | 0.945424 | 0.000123 | 747.383792 | 0.987130 | 58.625908 | 0.598412 | 0.970777 | 6.310863 | 15.796425 | 3.898390 | 15.829660 | 15.794705 | 1.184956 | 2.750728 | 0.648478 | 3.192182 | -0.554228 | 0.744976 | 0.997808 | 2.318545 | 1004.043093 | 39.391979 | 117.960948 | 138.194747 | 122.692949 | 57.603025 | 416.766964 | 26.077904 | 0.000000 | 6.641565 | 0.004169 | 0.120008 | 0.063621 | 0.055010 | 0.017411 | 8.471308 | 0.000000 | 6.814268 | 14.047403 | 1.196733 | 0.011926 | 7.697971 | 0.507171 | 0.058089 | 0.047104 | 1039.650738 | 882.680511 | 555.346326 | 4066.850479 | 4797.154633 | 0.140204 | 0.127942 | 0.252026 | 2.788882 | 1.235783 | 0.124397 | 0.400454 | 0.684330 | 0.120064 | 0.320113 | 0.576192 | 0.320113 | 0.778044 | 0.244718 | 0.394760 | 0.000000 | 0.000000 | 19.013257 | 0.546770 | 10.780543 | 26.661170 | 0.144815 | 7.365741 | 0.000000 | 17.936290 | 43.211418 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.287084 | 8.688487 | 20.092710 | 0.557359 | 11.532056 | 17.600192 | 7.839359 | 10.170463 | 30.073143 | 32.218169 | 9.050122 | 0.001281 | 20.376176 | 73.264316 | 0.029564 | 0.088866 | 0.056755 | 0.051432 | 0.060346 | 0.083268 | 0.081076 | 0.083484 | 0.071635 | 3.771465 | 0.003254 | 0.009213 | 0.060718 | 0.008821 | 122.846571 | 0.059370 | 1041.056588 | 0.000000 | 0.019125 | 0.017844 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.004791 | 0.004575 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.005755 | 1.729723 | 4.148742 | 0.053374 | 0.025171 | 0.001065 | 109.650967 | 0.004285 | 4.645115 | 0.033216 | 0.013943 | 0.403848 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.070587 | 19.504677 | 3.777866 | 29.260291 | 46.056598 | 41.298147 | 20.181246 | 136.292426 | 8.693213 | 0.000000 | 2.210744 | 0.001117 | 0.041057 | 0.018034 | 0.015094 | 0.005770 | 2.803984 | 0.000000 | 2.119795 | 4.260018 | 0.367529 | 0.003924 | 2.578596 | 0.123427 | 0.019926 | 0.014487 | 335.551157 | 401.814750 | 252.999118 | 1879.228369 | 2342.826978 | 0.063804 | 0.060267 | 0.118386 | 0.910146 | 0.403342 | 0.040344 | 0.132076 | 0.264917 | 0.048623 | 0.128921 | 0.218414 | 0.128921 | 0.304752 | 0.097344 | 0.160051 | 0.000000 | 0.000000 | 0.000000 | 5.976977 | 0.172629 | 3.188770 | 7.916036 | 0.043105 | 2.263727 | 0.000000 | 5.393420 | 13.332172 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.083232 | 2.593485 | 6.215866 | 0.168364 | 3.426925 | 9.736386 | 2.327482 | 3.037580 | 9.328958 | 14.673507 | 2.732094 | 0.000286 | 6.198508 | 23.217146 | 7.958376 | 5.770212 | 0.008914 | 0.024706 | 0.025252 | 0.023202 | 0.027584 | 0.023356 | 0.040331 | 0.041921 | 0.034543 | 1.298629 | 0.000999 | 0.002443 | 0.019840 | 0.002945 | 39.936406 | 0.018383 | 333.319601 | 0.000000 | 0.005199 | 0.004814 | 0.003773 | 0.003172 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001601 | 0.001571 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001826 | 0.541040 | 1.285448 | 0.011427 | 0.008281 | 0.000339 | 35.155091 | 0.001338 | 1.431868 | 0.010956 | 0.004533 | 0.133990 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.024208 | 6.730615 | 1.231997 | 5.340932 | 4.580430 | 4.929344 | 2.616086 | 30.911316 | 25.612690 | 0.000000 | 6.630616 | 3.404349 | 8.190905 | 320.259235 | 309.061299 | 1.821261 | 4.174524 | 0.000000 | 77.660446 | 3.315469 | 6.796312 | 1.233858 | 4.058501 | 4.220747 | 4.171844 | 18.421600 | 22.358305 | 99.367633 | 205.519304 | 14.733945 | 9.370666 | 7.513266 | 4.016785 | 54.701052 | 70.643942 | 11.526617 | 0.802081 | 1.345259 | 0.633941 | 0.895043 | 0.647090 | 1.175003 | 0.281895 | 0.332270 | 0.000000 | 0.000000 | 0.000000 | 5.346816 | 5.460971 | 7.883742 | 3.636633 | 12.325685 | 5.263666 | 0.000000 | 2.838380 | 29.197414 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.252091 | 224.173047 | 5.662293 | 5.367752 | 9.638797 | 137.888406 | 39.426847 | 37.637050 | 4.262573 | 20.132155 | 6.257921 | 0.128123 | 3.283394 | 75.538131 | 0.000000 | 318.418448 | 206.564196 | 215.288948 | 201.111728 | 302.506186 | 239.455326 | 352.616477 | 272.169707 | 51.354045 | 2.442673 | 8.170943 | 2.530046 | 0.956442 | 6.807826 | 29.865896 | 11.821030 | 0.000000 | 263.195864 | 240.981377 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 55.763508 | 275.979457 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.678898 | 1.738902 | 1.806273 | 11.728440 | 2.695999 | 11.610080 | 14.728866 | 0.453896 | 5.687782 | 5.560397 | 1.443457 | 6.395717 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.034235 | 1.942828 | 9.611628 | 0.111208 | 0.008471 | 0.002509 | 7.611403 | 1.039630 | 403.546477 | 75.679871 | 0.663256 | 17.013313 | 1.230712 | 0.276688 | 7.703874 | 0.503657 | 57.746537 | 4.216905 | 1.623070 | 0.995009 | 0.325708 | 0.072443 | 32.284956 | 262.729683 | 0.679641 | 6.444985 | 0.145610 | 2.610870 | 0.060086 | 2.452417 | 21.117674 | 530.523623 | 2.101836 | 28.450165 | 0.345636 | 9.162315 | 0.104729 | 5.563747 | 16.642363 | 0.021615 | 0.016829 | 0.005396 | 97.934373 | 0.500096 | 0.015318 | 0.003847 | 3.067826 | 0.021458 | 0.016475 | 0.005283 | 99.670066 | -0.867262 |
| std | 73.621787 | 80.407705 | 29.513152 | 441.691640 | 56.355540 | 0.000000 | 6.237214 | 0.008961 | 0.073897 | 0.015116 | 0.009302 | 0.012452 | 3.257276 | 0.000000 | 2.796596 | 17.221095 | 2.403867 | 0.012062 | 2.781041 | 0.217965 | 0.016737 | 626.822178 | 295.498535 | 1380.162148 | 2902.690117 | 0.177600 | 0.189495 | 1.244249 | 3.461181 | 0.408694 | 0.032944 | 0.535322 | 2.026549 | 1.344456 | 1.182618 | 2.574749 | 1.182619 | 0.304141 | 0.446756 | 1.807221 | 24.062943 | 2.360425 | 0.000000 | 6.234706 | 0.175038 | 7.849247 | 12.170315 | 0.189637 | 4.524251 | 0.000000 | 8.643985 | 60.925108 | 0.000000 | 0.054950 | 0.059581 | 25.749317 | 0.006807 | 0.004176 | 0.085095 | 9.532220 | 6.027889 | 0.274877 | 8.629022 | 7.119863 | 4.977467 | 7.121703 | 11.623078 | 307.502293 | 4.240095 | 0.000000 | 9.539190 | 31.651899 | 18.388481 | 17.629886 | 0.106190 | 0.022292 | 0.033203 | 0.031368 | 0.047872 | 0.023080 | 0.049226 | 0.017021 | 0.036074 | 0.516251 | 0.005051 | 0.002928 | 0.037332 | 0.012848 | 53.537262 | 0.052373 | 396.313662 | 0.087683 | 0.003231 | 0.003010 | 0.000174 | 0.000104 | 0.219578 | 0.000000 | 0.427110 | 0.062740 | 0.000356 | 0.000221 | 0.062968 | 0.003065 | 0.000851 | 0.003202 | 0.002988 | 0.087475 | 0.086758 | 0.008695 | 1.880087 | 2.105318 | 0.048939 | 0.012133 | 0.001668 | 48.949250 | 0.009497 | 6.485174 | 0.008102 | 0.008949 | 0.124304 | 0.099618 | 0.904120 | 0.108315 | 0.114144 | 0.280555 | 0.253471 | 0.135409 | 0.264175 | 1.220479 | 0.082531 | 0.002251 | 0.053181 | 6.537701 | 2.990476 | 57.544627 | 53.909792 | 52.253015 | 12.345358 | 263.300614 | 506.922106 | 0.000000 | 3.552254 | 0.001282 | 0.061343 | 0.026541 | 0.021844 | 0.027123 | 18.740631 | 0.000000 | 3.241843 | 31.002541 | 23.364063 | 0.009346 | 5.239219 | 1.122427 | 0.079174 | 0.039538 | 406.848810 | 983.043021 | 574.808588 | 4239.245058 | 6553.569317 | 0.121989 | 0.242534 | 0.407329 | 1.119756 | 0.632767 | 0.047639 | 0.197918 | 0.157468 | 0.060785 | 0.071243 | 0.095734 | 0.071247 | 0.116322 | 0.074918 | 0.282903 | 0.000000 | 0.000000 | 3.311632 | 0.224402 | 4.164051 | 6.836101 | 0.110198 | 7.188720 | 0.000000 | 8.609912 | 21.711876 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.395187 | 15.720926 | 10.552162 | 0.537705 | 16.445556 | 8.690718 | 5.104495 | 14.622904 | 17.461798 | 565.101239 | 11.541083 | 0.050621 | 17.497556 | 28.067143 | 1.168074 | 0.042065 | 0.025005 | 0.031578 | 0.053030 | 0.056456 | 0.030437 | 0.025764 | 0.046283 | 1.170436 | 0.001646 | 0.001989 | 0.023305 | 0.055937 | 55.156003 | 0.071211 | 433.170076 | 0.000000 | 0.010756 | 0.010745 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001698 | 0.001441 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.084618 | 4.335614 | 10.045084 | 0.066880 | 0.049235 | 0.015771 | 54.597274 | 0.037472 | 64.354756 | 0.022425 | 0.009132 | 0.120334 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.029649 | 7.344404 | 1.152329 | 8.402013 | 17.866438 | 17.737513 | 3.830463 | 85.607784 | 168.949413 | 0.000000 | 1.196437 | 0.000340 | 0.020289 | 0.006483 | 0.005545 | 0.008550 | 5.864324 | 0.000000 | 0.962923 | 9.763829 | 7.386343 | 0.002936 | 1.616993 | 0.270987 | 0.025549 | 0.011494 | 137.692483 | 477.050076 | 283.530702 | 1975.111365 | 3226.924298 | 0.064225 | 0.130825 | 0.219147 | 0.331982 | 0.197514 | 0.014511 | 0.064867 | 0.057387 | 0.025400 | 0.027468 | 0.033593 | 0.027470 | 0.043460 | 0.028796 | 0.117316 | 0.000000 | 0.000000 | 0.000000 | 1.018629 | 0.072392 | 1.215930 | 2.179059 | 0.031885 | 2.116994 | 0.000000 | 2.518859 | 6.615850 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.063425 | 5.645226 | 3.403447 | 0.172883 | 5.781558 | 7.556131 | 1.699435 | 5.645022 | 6.074702 | 261.738451 | 3.667902 | 0.011319 | 5.371825 | 8.895221 | 17.512965 | 17.077498 | 0.352186 | 0.011862 | 0.010603 | 0.014326 | 0.024563 | 0.013157 | 0.015499 | 0.013068 | 0.022307 | 0.386918 | 0.000501 | 0.000395 | 0.007136 | 0.020003 | 17.056304 | 0.021644 | 138.801928 | 0.000000 | 0.002656 | 0.002382 | 0.002699 | 0.002107 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000534 | 0.000467 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.026740 | 1.341020 | 3.168427 | 0.014366 | 0.015488 | 0.004989 | 17.227003 | 0.011816 | 20.326415 | 0.006738 | 0.002956 | 0.038408 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.010728 | 2.829583 | 0.364711 | 2.578118 | 1.776843 | 2.122978 | 0.551474 | 18.413622 | 47.308463 | 0.000000 | 3.958371 | 1.035433 | 4.054515 | 287.704482 | 325.448391 | 3.057692 | 6.913855 | 0.000000 | 32.596933 | 6.325365 | 23.257716 | 0.995620 | 3.042144 | 10.632730 | 6.435390 | 36.060084 | 36.395408 | 126.188715 | 225.778870 | 34.108854 | 34.369789 | 34.557804 | 1.611274 | 34.108051 | 38.376178 | 6.169471 | 0.184213 | 0.659195 | 0.143552 | 0.155522 | 0.141252 | 0.176158 | 0.086461 | 0.236275 | 0.000000 | 0.000000 | 0.000000 | 0.919196 | 2.250804 | 3.059660 | 0.938372 | 8.125876 | 4.537737 | 0.000000 | 1.345576 | 13.335189 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 8.673724 | 230.766915 | 3.151685 | 4.983367 | 10.174117 | 47.698041 | 22.457104 | 24.822918 | 2.611174 | 14.939590 | 10.185026 | 5.062075 | 2.638608 | 35.752493 | 0.000000 | 281.011323 | 192.864413 | 213.126638 | 218.690015 | 287.364070 | 263.837645 | 252.043751 | 228.046702 | 18.048612 | 1.224283 | 1.759262 | 0.973948 | 6.615200 | 3.260019 | 24.621586 | 4.956647 | 0.000000 | 324.771342 | 323.003410 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 37.691736 | 329.664680 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 10.783880 | 4.890663 | 4.715894 | 15.814420 | 5.702366 | 103.122996 | 7.104435 | 4.147581 | 20.663414 | 3.920370 | 0.958428 | 1.888698 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.252913 | 0.731928 | 2.896376 | 0.002737 | 0.001534 | 0.000296 | 1.315544 | 0.389066 | 5.063887 | 3.390523 | 0.673346 | 4.966954 | 1.361117 | 0.276231 | 2.192647 | 0.598852 | 35.207552 | 1.280008 | 1.870433 | 0.083860 | 0.201392 | 0.051578 | 19.026081 | 7.630585 | 0.121758 | 2.633583 | 0.081122 | 1.032761 | 0.032761 | 0.996644 | 10.213294 | 17.499736 | 0.275112 | 86.304681 | 0.248478 | 26.920150 | 0.067791 | 16.921369 | 12.485267 | 0.011730 | 0.009640 | 0.003116 | 87.520966 | 0.003404 | 0.017180 | 0.003720 | 3.578033 | 0.012358 | 0.008808 | 0.002867 | 93.891919 | 0.498010 |
| min | 2743.240000 | 2158.750000 | 2060.660000 | 0.000000 | 0.681500 | 100.000000 | 82.131100 | 0.000000 | 1.191000 | -0.053400 | -0.034900 | 0.655400 | 182.094000 | 0.000000 | 2.249300 | 333.448600 | 4.469600 | 0.579400 | 169.177400 | 9.877300 | 1.179700 | -7150.250000 | 0.000000 | -9986.750000 | -14804.500000 | 0.000000 | 0.000000 | 0.000000 | 59.400000 | 0.666700 | 0.034100 | 2.069800 | 83.182900 | 7.603200 | 49.834800 | 63.677400 | 40.228900 | 64.919300 | 84.732700 | 111.712800 | 1.434000 | -0.075900 | 70.000000 | 342.754500 | 9.464000 | 108.846400 | 699.813900 | 0.496700 | 125.798200 | 1.000000 | 607.392700 | 40.261400 | 0.000000 | 3.706000 | 3.932000 | 2801.000000 | 0.875500 | 0.931900 | 4.219900 | -28.988200 | 324.714500 | 9.461100 | 81.490000 | 1.659100 | 6.448200 | 4.308000 | 632.422600 | 0.413700 | 87.025500 | 1.000000 | 581.777300 | 21.433200 | -59.477700 | 456.044700 | 0.000000 | -0.104900 | -0.186200 | -0.104600 | -0.348200 | -0.056800 | -0.143700 | -0.098200 | -0.212900 | 5.825700 | 0.117400 | 0.105300 | 2.242500 | 0.774900 | 1627.471400 | 0.111300 | 7397.310000 | -0.357000 | -0.012600 | -0.017100 | -0.002000 | -0.000900 | -1.480300 | 0.000000 | -5.271700 | -0.528300 | -0.003000 | -0.002400 | -0.535300 | -0.032900 | -0.011900 | -0.028100 | -0.013300 | -0.522600 | -0.345400 | 0.784800 | 88.193800 | 213.008300 | 0.000000 | 0.853400 | 0.000000 | 544.025400 | 0.890000 | 52.806800 | 0.527400 | 0.841100 | 5.125900 | 15.460000 | 1.671000 | 15.170000 | 15.430000 | 0.312200 | 2.340000 | 0.316100 | 0.000000 | -3.779000 | 0.419900 | 0.993600 | 2.191100 | 980.451000 | 33.365800 | 58.000000 | 36.100000 | 19.200000 | 19.800000 | 0.000000 | 0.031900 | 0.000000 | 1.740000 | 0.000000 | 0.032400 | 0.021400 | 0.022700 | 0.004300 | 1.420800 | 0.000000 | 1.337000 | 2.020000 | 0.154400 | 0.003600 | 1.243800 | 0.140000 | 0.011100 | 0.011800 | 234.099600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.800000 | 0.300000 | 0.033000 | 0.046000 | 0.297900 | 0.008900 | 0.128700 | 0.253800 | 0.128700 | 0.461600 | 0.073500 | 0.047000 | 0.000000 | 0.000000 | 9.400000 | 0.093000 | 3.170000 | 5.014000 | 0.029700 | 1.940000 | 0.000000 | 6.220000 | 6.613000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.080000 | 1.750000 | 9.220000 | 0.090000 | 2.770000 | 3.210000 | 0.000000 | 0.000000 | 7.728000 | 0.042900 | 2.300000 | 0.000000 | 4.010000 | 5.359000 | 0.000000 | 0.031900 | 0.002200 | 0.007100 | 0.003700 | 0.019300 | 0.005900 | 0.009700 | 0.007900 | 1.034000 | 0.000700 | 0.005700 | 0.020000 | 0.000300 | 32.263700 | 0.009300 | 168.799800 | 0.000000 | 0.006200 | 0.007200 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001300 | 0.001400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000300 | 0.291400 | 1.102200 | 0.000000 | 0.003000 | 0.000000 | 21.010700 | 0.000300 | 0.767300 | 0.009400 | 0.001700 | 0.126900 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.019800 | 6.098000 | 1.301700 | 15.547100 | 10.401500 | 6.943100 | 8.651200 | 0.000000 | 0.011100 | 0.000000 | 0.561500 | 0.000000 | 0.010700 | 0.007300 | 0.006900 | 0.001600 | 0.505000 | 0.000000 | 0.461100 | 0.728000 | 0.051300 | 0.001200 | 0.396000 | 0.041600 | 0.003800 | 0.004100 | 82.323300 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.310000 | 0.111800 | 0.010800 | 0.013800 | 0.117100 | 0.003400 | 0.054900 | 0.091300 | 0.054900 | 0.180900 | 0.032800 | 0.022400 | 0.000000 | 0.000000 | 0.000000 | 2.788200 | 0.028300 | 0.984800 | 1.657400 | 0.008400 | 0.611400 | 0.000000 | 1.710100 | 2.234500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.022400 | 0.537300 | 2.837200 | 0.028200 | 0.789900 | 5.215100 | 0.000000 | 0.000000 | 2.200100 | 0.013100 | 0.574100 | 0.000000 | 1.256500 | 2.056000 | 1.769400 | 1.017700 | 0.000000 | 0.010300 | 0.001000 | 0.002900 | 0.002000 | 0.005600 | 0.002600 | 0.004000 | 0.003800 | 0.379600 | 0.000300 | 0.001700 | 0.007600 | 0.000100 | 10.720400 | 0.002800 | 60.988200 | 0.000000 | 0.001700 | 0.002000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000400 | 0.000400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000100 | 0.087500 | 0.338300 | 0.000000 | 0.000800 | 0.000000 | 6.310100 | 0.000100 | 0.304600 | 0.003100 | 0.000500 | 0.034200 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.006200 | 2.054500 | 0.424000 | 2.737800 | 1.216300 | 0.734200 | 0.960900 | 0.000000 | 4.041600 | 0.000000 | 1.534000 | 0.000000 | 2.153100 | 0.000000 | 0.000000 | 0.441100 | 0.721700 | 0.000000 | 23.020000 | 0.486600 | 1.466600 | 0.363200 | 0.663700 | 1.119800 | 0.783700 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.156800 | 0.000000 | 14.120600 | 1.097300 | 0.351200 | 0.097400 | 0.216900 | 0.333600 | 0.308600 | 0.696800 | 0.084600 | 0.039900 | 0.000000 | 0.000000 | 0.000000 | 2.670900 | 0.903700 | 2.329400 | 0.694800 | 3.048900 | 1.442800 | 0.000000 | 0.991000 | 7.953400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.716300 | 0.000000 | 2.600900 | 0.832500 | 2.402600 | 11.499700 | 0.000000 | 0.000000 | 1.101100 | 0.000000 | 1.687200 | 0.000000 | 0.645900 | 8.840600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 13.722500 | 0.555800 | 4.888200 | 0.833000 | 0.034200 | 1.772000 | 4.813500 | 1.949600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.028700 | 0.288000 | 0.467400 | 0.000000 | 0.312100 | 0.000000 | 2.681100 | 0.025800 | 1.310400 | 1.540000 | 0.170500 | 2.170000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.851600 | 0.614400 | 3.276100 | 0.105300 | 0.005100 | 0.001600 | 4.429400 | 0.444400 | 372.822000 | 71.038000 | 0.044600 | 6.110000 | 0.120000 | 0.018700 | 2.786000 | 0.052000 | 4.826900 | 1.496700 | 0.164600 | 0.891900 | 0.069900 | 0.017700 | 7.236900 | 242.286000 | 0.304900 | 0.970000 | 0.022400 | 0.412200 | 0.009100 | 0.370600 | 3.250400 | 317.196400 | 0.980200 | 3.540000 | 0.066700 | 1.039500 | 0.023000 | 0.663600 | 4.582000 | -0.016900 | 0.003200 | 0.001000 | 0.000000 | 0.477800 | 0.006000 | 0.001700 | 1.197500 | -0.016900 | 0.003200 | 0.001000 | 0.000000 | -1.000000 |
| 25% | 2966.260000 | 2452.247500 | 2181.044400 | 1081.875800 | 1.017700 | 100.000000 | 97.920000 | 0.121100 | 1.411200 | -0.010800 | -0.005600 | 0.958100 | 198.130700 | 0.000000 | 7.094875 | 406.127400 | 9.567625 | 0.968200 | 188.299825 | 12.460000 | 1.396500 | -5933.250000 | 2578.000000 | -4371.750000 | -1476.000000 | 1.094800 | 1.906500 | 5.263700 | 67.377800 | 2.088900 | 0.161700 | 3.362700 | 84.490500 | 8.580000 | 50.252350 | 64.024800 | 49.421200 | 66.040650 | 86.578300 | 118.015600 | 74.800000 | 2.690000 | 70.000000 | 350.801575 | 9.925425 | 130.728875 | 724.442300 | 0.985000 | 136.926800 | 1.000000 | 625.928425 | 115.508975 | 0.000000 | 4.574000 | 4.816000 | 2836.000000 | 0.925450 | 0.946650 | 4.531900 | -1.871575 | 350.596400 | 10.283000 | 112.022700 | 10.364300 | 17.364800 | 23.056425 | 698.770200 | 0.890700 | 145.237300 | 1.000000 | 612.774500 | 87.484200 | 145.305300 | 464.458100 | 0.000000 | -0.019550 | -0.051900 | -0.029500 | -0.047600 | -0.010800 | -0.044500 | -0.027200 | -0.018000 | 7.104225 | 0.129800 | 0.110725 | 2.376850 | 0.975800 | 1777.470300 | 0.169375 | 8564.689975 | -0.042900 | -0.001200 | -0.001600 | -0.000100 | 0.000000 | -0.088600 | 0.000000 | -0.218800 | -0.029800 | -0.000200 | -0.000100 | -0.035700 | -0.011800 | -0.000400 | -0.001900 | -0.001000 | -0.048600 | -0.064900 | 0.978800 | 100.389000 | 230.373800 | 0.459300 | 0.938600 | 0.000000 | 721.023000 | 0.989500 | 57.978300 | 0.594100 | 0.964800 | 6.246400 | 15.730000 | 3.202000 | 15.762500 | 15.722500 | 0.974400 | 2.572000 | 0.548900 | 3.074000 | -0.898800 | 0.688700 | 0.996400 | 2.277300 | 999.996100 | 37.347250 | 92.000000 | 90.000000 | 81.300000 | 50.900100 | 243.786000 | 0.131700 | 0.000000 | 5.110000 | 0.003300 | 0.083900 | 0.048000 | 0.042300 | 0.010000 | 6.359900 | 0.000000 | 4.459250 | 8.089750 | 0.373750 | 0.007275 | 5.926950 | 0.240000 | 0.036250 | 0.027050 | 721.675050 | 411.000000 | 295.000000 | 1321.000000 | 451.000000 | 0.091000 | 0.068000 | 0.132000 | 2.100000 | 0.900000 | 0.090000 | 0.230000 | 0.575600 | 0.079800 | 0.276600 | 0.516800 | 0.276500 | 0.692200 | 0.196250 | 0.222000 | 0.000000 | 0.000000 | 16.850000 | 0.378000 | 7.732500 | 21.171500 | 0.102200 | 5.390000 | 0.000000 | 14.505000 | 24.711000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.218000 | 5.040000 | 17.130000 | 0.296000 | 6.740000 | 14.155000 | 5.020000 | 6.094000 | 24.653000 | 0.114300 | 6.040000 | 0.000000 | 16.350000 | 56.158000 | 0.000000 | 0.065600 | 0.043800 | 0.032500 | 0.036400 | 0.056800 | 0.063200 | 0.069550 | 0.045800 | 2.946100 | 0.002300 | 0.007800 | 0.040200 | 0.001400 | 95.147350 | 0.029775 | 718.725350 | 0.000000 | 0.013200 | 0.012600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.003700 | 0.003600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001200 | 0.911500 | 2.725900 | 0.019200 | 0.014700 | 0.000000 | 76.132150 | 0.000700 | 2.205650 | 0.024500 | 0.004700 | 0.307600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.044000 | 13.828000 | 2.956500 | 24.982300 | 30.013900 | 27.092725 | 18.247100 | 81.215600 | 0.044700 | 0.000000 | 1.697700 | 0.000900 | 0.028300 | 0.014200 | 0.011900 | 0.003300 | 2.210400 | 0.000000 | 1.438175 | 2.467200 | 0.114875 | 0.002400 | 2.092125 | 0.064900 | 0.012500 | 0.008725 | 229.809450 | 185.089800 | 130.220300 | 603.032900 | 210.936600 | 0.040700 | 0.030200 | 0.058900 | 0.717200 | 0.295800 | 0.030000 | 0.072800 | 0.225000 | 0.033100 | 0.113700 | 0.197600 | 0.113700 | 0.278550 | 0.077600 | 0.091500 | 0.000000 | 0.000000 | 0.000000 | 5.301525 | 0.117375 | 2.319725 | 6.245150 | 0.031200 | 1.670075 | 0.000000 | 4.272950 | 7.578600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.068800 | 1.546550 | 5.453900 | 0.089400 | 2.035700 | 8.288525 | 1.542850 | 1.901350 | 7.588900 | 0.034600 | 1.911800 | 0.000000 | 4.998900 | 17.860900 | 4.440600 | 2.532700 | 0.000000 | 0.018000 | 0.019600 | 0.014600 | 0.016600 | 0.016000 | 0.030200 | 0.034850 | 0.021200 | 1.025475 | 0.000700 | 0.002200 | 0.013800 | 0.000400 | 32.168700 | 0.009500 | 228.682525 | 0.000000 | 0.003800 | 0.003500 | 0.002600 | 0.002200 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001300 | 0.001300 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000400 | 0.295500 | 0.842300 | 0.005300 | 0.004800 | 0.000000 | 24.386550 | 0.000200 | 0.675150 | 0.008300 | 0.001500 | 0.104400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.014000 | 4.547600 | 0.966500 | 4.127800 | 3.012800 | 3.265075 | 2.321300 | 18.407900 | 11.375800 | 0.000000 | 4.927400 | 2.660100 | 5.765500 | 0.000000 | 0.000000 | 1.030400 | 3.184200 | 0.000000 | 55.976675 | 1.965250 | 3.766200 | 0.743425 | 3.113225 | 1.935500 | 2.571400 | 6.999700 | 11.059000 | 31.032400 | 10.027100 | 7.550700 | 3.494400 | 1.950900 | 3.070700 | 36.290300 | 48.173800 | 5.414100 | 0.679600 | 0.907650 | 0.550500 | 0.804800 | 0.555800 | 1.046800 | 0.226100 | 0.187700 | 0.000000 | 0.000000 | 0.000000 | 4.764200 | 3.747875 | 5.806525 | 2.899675 | 8.816575 | 3.827525 | 0.000000 | 2.291175 | 20.221850 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.697500 | 38.472775 | 4.847200 | 2.823300 | 5.807300 | 105.525150 | 24.900800 | 23.156500 | 3.494500 | 11.577100 | 4.105400 | 0.000000 | 2.627700 | 52.894500 | 0.000000 | 0.000000 | 81.316150 | 76.455400 | 50.383550 | 0.000000 | 55.555150 | 139.914350 | 112.859250 | 38.391100 | 1.747100 | 6.924650 | 1.663750 | 0.139000 | 5.274600 | 16.342300 | 8.150350 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 35.322200 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.121500 | 0.890300 | 1.171200 | 4.160300 | 1.552150 | 0.000000 | 10.182800 | 0.073050 | 3.769650 | 4.101500 | 0.484200 | 4.895450 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.889900 | 1.385300 | 7.495750 | 0.109600 | 0.007800 | 0.002400 | 7.116000 | 0.797500 | 400.694000 | 73.254000 | 0.226250 | 14.530000 | 0.870000 | 0.094900 | 6.738100 | 0.343800 | 27.017600 | 3.625100 | 1.182900 | 0.955200 | 0.149825 | 0.036200 | 15.762450 | 259.972500 | 0.567100 | 4.980000 | 0.087700 | 2.090200 | 0.038200 | 1.884400 | 15.466200 | 530.702700 | 1.982900 | 7.500000 | 0.242250 | 2.567850 | 0.075100 | 1.408450 | 11.501550 | 0.013800 | 0.010600 | 0.003400 | 46.184900 | 0.497900 | 0.011600 | 0.003100 | 2.306500 | 0.013425 | 0.010600 | 0.003300 | 44.368600 | -1.000000 |
| 50% | 3011.490000 | 2499.405000 | 2201.066700 | 1285.214400 | 1.316800 | 100.000000 | 101.512200 | 0.122400 | 1.461600 | -0.001300 | 0.000400 | 0.965800 | 199.535600 | 0.000000 | 8.967000 | 412.219100 | 9.851750 | 0.972600 | 189.664200 | 12.499600 | 1.406000 | -5523.250000 | 2664.000000 | -3820.750000 | -78.750000 | 1.283000 | 1.986500 | 7.264700 | 69.155600 | 2.377800 | 0.186700 | 3.431000 | 85.135450 | 8.769800 | 50.396400 | 64.165800 | 49.603600 | 66.231800 | 86.820700 | 118.399300 | 78.290000 | 3.074000 | 70.000000 | 353.720900 | 10.034850 | 136.400000 | 733.450000 | 1.251050 | 140.007750 | 1.000000 | 631.370900 | 183.318150 | 0.000000 | 4.596000 | 4.843000 | 2854.000000 | 0.931000 | 0.949300 | 4.572700 | 0.947250 | 353.799100 | 10.436700 | 116.211800 | 13.246050 | 20.021350 | 26.261450 | 706.453600 | 0.978300 | 147.597300 | 1.000000 | 619.032700 | 102.604300 | 152.297200 | 466.081700 | 0.000000 | -0.006300 | -0.028900 | -0.009900 | -0.012500 | 0.000600 | -0.008700 | -0.019600 | 0.007600 | 7.467450 | 0.133000 | 0.113550 | 2.403900 | 0.987400 | 1809.249200 | 0.190100 | 8825.435100 | 0.000000 | 0.000400 | -0.000200 | 0.000000 | 0.000000 | 0.003900 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -0.010100 | 0.000000 | -0.000200 | 0.000200 | 0.000000 | -0.011200 | 0.981000 | 101.481700 | 231.201200 | 0.462850 | 0.946400 | 0.000000 | 750.861400 | 0.990500 | 58.549100 | 0.599000 | 0.969400 | 6.313600 | 15.790000 | 3.877000 | 15.830000 | 15.780000 | 1.144000 | 2.735000 | 0.653900 | 3.195000 | -0.141900 | 0.758750 | 0.997750 | 2.312400 | 1004.050000 | 38.902600 | 109.000000 | 134.600000 | 117.700000 | 55.900100 | 339.561000 | 0.235800 | 0.000000 | 6.260000 | 0.003900 | 0.107500 | 0.058600 | 0.050000 | 0.015900 | 7.917300 | 0.000000 | 5.951000 | 10.993500 | 0.468700 | 0.011100 | 7.512700 | 0.320000 | 0.048700 | 0.035450 | 1020.300050 | 623.000000 | 438.000000 | 2614.000000 | 1784.000000 | 0.120000 | 0.089000 | 0.184000 | 2.600000 | 1.200000 | 0.119000 | 0.412000 | 0.686000 | 0.112500 | 0.323850 | 0.577600 | 0.323850 | 0.768200 | 0.242900 | 0.299000 | 0.000000 | 0.000000 | 18.690000 | 0.524000 | 10.170000 | 27.200500 | 0.132600 | 6.735000 | 0.000000 | 17.865000 | 40.209500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.259000 | 6.780000 | 19.370000 | 0.424000 | 8.570000 | 17.235000 | 6.760000 | 8.462000 | 30.097000 | 0.158200 | 7.740000 | 0.000000 | 19.720000 | 73.248000 | 0.000000 | 0.079700 | 0.053200 | 0.041600 | 0.056000 | 0.075400 | 0.082500 | 0.084600 | 0.061700 | 3.630750 | 0.003000 | 0.008950 | 0.060900 | 0.002300 | 119.436000 | 0.039800 | 967.299800 | 0.000000 | 0.016500 | 0.015500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.004600 | 0.004400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001700 | 1.185100 | 3.673000 | 0.027000 | 0.021000 | 0.000000 | 103.093600 | 0.001000 | 2.864600 | 0.030800 | 0.015000 | 0.405100 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.070600 | 17.977000 | 3.703500 | 28.773500 | 45.676500 | 40.019250 | 19.580900 | 110.601400 | 0.078400 | 0.000000 | 2.083100 | 0.001100 | 0.037200 | 0.016900 | 0.013900 | 0.005300 | 2.658000 | 0.000000 | 1.875150 | 3.360050 | 0.138950 | 0.003600 | 2.549000 | 0.083300 | 0.016900 | 0.011000 | 317.867100 | 278.671900 | 195.825600 | 1202.412100 | 820.098800 | 0.052800 | 0.040000 | 0.082800 | 0.860400 | 0.380800 | 0.038800 | 0.137200 | 0.264300 | 0.044800 | 0.129500 | 0.219450 | 0.129500 | 0.302900 | 0.097700 | 0.121500 | 0.000000 | 0.000000 | 0.000000 | 5.831500 | 0.163400 | 2.898900 | 8.388800 | 0.039850 | 2.077650 | 0.000000 | 5.458800 | 12.504500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.084800 | 2.062700 | 5.980100 | 0.129400 | 2.513500 | 9.073550 | 2.054450 | 2.560850 | 9.474200 | 0.046400 | 2.377300 | 0.000000 | 6.005600 | 23.214700 | 5.567000 | 3.046400 | 0.000000 | 0.022600 | 0.024000 | 0.018800 | 0.025300 | 0.022000 | 0.042100 | 0.044200 | 0.029400 | 1.255300 | 0.000900 | 0.002400 | 0.019600 | 0.000700 | 39.696100 | 0.012500 | 309.831650 | 0.000000 | 0.004600 | 0.004300 | 0.003200 | 0.002800 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001600 | 0.001500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000500 | 0.372600 | 1.106300 | 0.006800 | 0.006800 | 0.000000 | 32.530700 | 0.000300 | 0.877300 | 0.010200 | 0.004900 | 0.133900 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023900 | 5.920100 | 1.239700 | 4.922450 | 4.489700 | 4.732750 | 2.548100 | 26.156900 | 20.255100 | 0.000000 | 6.176600 | 3.234000 | 7.395600 | 302.177600 | 272.448700 | 1.645100 | 3.943100 | 0.000000 | 69.905450 | 2.667100 | 4.764400 | 1.135300 | 3.941450 | 2.534100 | 3.453800 | 11.105600 | 16.381000 | 57.969300 | 151.115600 | 10.197700 | 4.551100 | 2.764300 | 3.780900 | 49.090900 | 65.437800 | 12.085900 | 0.807600 | 1.264550 | 0.643500 | 0.902700 | 0.651100 | 1.163800 | 0.279700 | 0.251200 | 0.000000 | 0.000000 | 0.000000 | 5.271450 | 5.227100 | 7.424900 | 3.724500 | 11.350900 | 4.793350 | 0.000000 | 2.830350 | 26.167850 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 5.645000 | 150.340100 | 5.472400 | 4.061100 | 7.396000 | 138.255150 | 34.246750 | 32.820050 | 4.276200 | 15.973800 | 5.242200 | 0.000000 | 3.184500 | 70.434500 | 0.000000 | 293.518500 | 148.317500 | 138.775500 | 112.953400 | 249.927000 | 112.275500 | 348.529400 | 219.487200 | 48.557450 | 2.250800 | 8.008950 | 2.529100 | 0.232500 | 6.607900 | 22.039100 | 10.906550 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 46.986100 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.174700 | 1.154300 | 1.589100 | 5.832950 | 2.221000 | 0.000000 | 13.742600 | 0.100000 | 4.877100 | 5.134200 | 1.550100 | 6.410800 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.054800 | 1.785500 | 9.459300 | 0.109600 | 0.007800 | 0.002600 | 7.116000 | 0.911100 | 403.122000 | 74.084000 | 0.471000 | 16.340000 | 1.150000 | 0.197900 | 7.427900 | 0.478900 | 54.441700 | 4.067100 | 1.529800 | 0.972700 | 0.290900 | 0.059200 | 29.731150 | 264.272000 | 0.651000 | 5.160000 | 0.119550 | 2.150450 | 0.048650 | 1.999700 | 16.988350 | 532.398200 | 2.118600 | 8.650000 | 0.293400 | 2.975800 | 0.089500 | 1.624500 | 13.817900 | 0.020400 | 0.014800 | 0.004700 | 72.288900 | 0.500200 | 0.013800 | 0.003600 | 2.757650 | 0.020500 | 0.014800 | 0.004600 | 71.900500 | -1.000000 |
| 75% | 3056.650000 | 2538.822500 | 2218.055500 | 1591.223500 | 1.525700 | 100.000000 | 104.586700 | 0.123800 | 1.516900 | 0.008400 | 0.005900 | 0.971300 | 202.007100 | 0.000000 | 10.861875 | 419.089275 | 10.128175 | 0.976800 | 192.189375 | 12.547100 | 1.415000 | -5356.250000 | 2841.750000 | -3352.750000 | 1377.250000 | 1.304300 | 2.003200 | 7.329700 | 72.266700 | 2.655600 | 0.207100 | 3.531300 | 85.741900 | 9.060600 | 50.578800 | 64.344700 | 49.747650 | 66.343275 | 87.002400 | 118.939600 | 80.200000 | 3.521000 | 70.000000 | 360.772250 | 10.152475 | 142.098225 | 741.454500 | 1.340350 | 143.195700 | 1.000000 | 638.136325 | 206.977150 | 0.000000 | 4.617000 | 4.869000 | 2874.000000 | 0.933100 | 0.952000 | 4.668600 | 4.385225 | 359.673600 | 10.591600 | 120.927300 | 16.376100 | 22.813625 | 29.914950 | 714.597000 | 1.065000 | 149.959100 | 1.000000 | 625.170000 | 115.498900 | 158.437800 | 467.889900 | 0.000000 | 0.007100 | -0.006500 | 0.009250 | 0.012200 | 0.013200 | 0.009100 | -0.012000 | 0.026900 | 7.807625 | 0.136300 | 0.114900 | 2.428600 | 0.989700 | 1841.873000 | 0.200425 | 9065.432400 | 0.050700 | 0.002000 | 0.001000 | 0.000100 | 0.000100 | 0.122000 | 0.000000 | 0.189300 | 0.029800 | 0.000200 | 0.000100 | 0.033600 | -0.008200 | 0.000400 | 0.001100 | 0.001600 | 0.049000 | 0.038000 | 0.982300 | 102.078100 | 233.036100 | 0.466425 | 0.952300 | 0.000000 | 776.781850 | 0.990900 | 59.133900 | 0.603400 | 0.978300 | 6.375850 | 15.860000 | 4.392000 | 15.900000 | 15.870000 | 1.338000 | 2.873000 | 0.713500 | 3.311000 | 0.047300 | 0.814500 | 0.998900 | 2.358300 | 1008.670600 | 40.804600 | 127.000000 | 181.000000 | 161.600000 | 62.900100 | 502.205900 | 0.439100 | 0.000000 | 7.500000 | 0.004900 | 0.132700 | 0.071800 | 0.061500 | 0.021300 | 9.585300 | 0.000000 | 8.275000 | 14.347250 | 0.679925 | 0.014900 | 9.054675 | 0.450000 | 0.066700 | 0.048875 | 1277.750125 | 966.000000 | 625.000000 | 5034.000000 | 6384.000000 | 0.154000 | 0.116000 | 0.255000 | 3.200000 | 1.500000 | 0.151000 | 0.536000 | 0.797300 | 0.140300 | 0.370200 | 0.634500 | 0.370200 | 0.843900 | 0.293925 | 0.423000 | 0.000000 | 0.000000 | 20.972500 | 0.688750 | 13.337500 | 31.687000 | 0.169150 | 8.450000 | 0.000000 | 20.860000 | 57.674750 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.296000 | 9.555000 | 21.460000 | 0.726000 | 11.460000 | 20.162500 | 9.490000 | 11.953000 | 33.506000 | 0.230700 | 9.940000 | 0.000000 | 22.370000 | 90.515000 | 0.000000 | 0.099450 | 0.064200 | 0.062450 | 0.073700 | 0.093550 | 0.098300 | 0.097550 | 0.086350 | 4.404750 | 0.003800 | 0.010300 | 0.076500 | 0.005500 | 144.502800 | 0.061300 | 1261.299800 | 0.000000 | 0.021200 | 0.020000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.005700 | 0.005300 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.002600 | 1.761800 | 4.479700 | 0.051500 | 0.027300 | 0.000000 | 131.758400 | 0.001300 | 3.795050 | 0.037900 | 0.021300 | 0.480950 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.091650 | 24.653000 | 4.379400 | 31.702200 | 59.594700 | 54.277325 | 22.097300 | 162.038200 | 0.144900 | 0.000000 | 2.514300 | 0.001300 | 0.045800 | 0.020700 | 0.016600 | 0.007100 | 3.146200 | 0.000000 | 2.606950 | 4.311425 | 0.198450 | 0.004900 | 3.024525 | 0.118100 | 0.023600 | 0.014925 | 403.989300 | 428.554500 | 273.952600 | 2341.288700 | 3190.616400 | 0.069200 | 0.052000 | 0.115500 | 1.046400 | 0.477000 | 0.048600 | 0.178500 | 0.307500 | 0.055200 | 0.147600 | 0.237900 | 0.147600 | 0.331900 | 0.115900 | 0.160175 | 0.000000 | 0.000000 | 0.000000 | 6.547800 | 0.218100 | 4.021250 | 9.481100 | 0.050200 | 2.633350 | 0.000000 | 6.344875 | 17.925175 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.095600 | 2.790525 | 6.549500 | 0.210400 | 3.360400 | 10.041625 | 2.785475 | 3.405450 | 10.439900 | 0.066800 | 2.985400 | 0.000000 | 6.885200 | 28.873100 | 6.825500 | 4.085700 | 0.000000 | 0.027300 | 0.028600 | 0.028500 | 0.033900 | 0.026900 | 0.050200 | 0.050000 | 0.042300 | 1.533325 | 0.001100 | 0.002700 | 0.025000 | 0.001800 | 47.079200 | 0.018600 | 412.329775 | 0.000000 | 0.005800 | 0.005400 | 0.004200 | 0.003600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.001900 | 0.001800 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000800 | 0.541200 | 1.386600 | 0.011325 | 0.009300 | 0.000000 | 42.652450 | 0.000400 | 1.148200 | 0.012400 | 0.006900 | 0.160400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.032300 | 8.585200 | 1.416700 | 5.787100 | 5.936700 | 6.458300 | 2.853200 | 38.139700 | 29.307300 | 0.000000 | 7.570700 | 4.010700 | 9.168800 | 524.002200 | 582.935200 | 2.214700 | 4.784300 | 0.000000 | 92.911500 | 3.470975 | 6.883500 | 1.539500 | 4.768650 | 3.609000 | 4.755800 | 17.423100 | 21.765200 | 120.172900 | 305.026300 | 12.754200 | 5.822800 | 3.822200 | 4.678600 | 66.666700 | 84.973400 | 15.796400 | 0.927600 | 1.577825 | 0.733425 | 0.988800 | 0.748400 | 1.272300 | 0.338825 | 0.351100 | 0.000000 | 0.000000 | 0.000000 | 5.913000 | 6.902475 | 9.576775 | 4.341925 | 14.387900 | 6.089450 | 0.000000 | 3.309225 | 35.278800 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.386900 | 335.922400 | 6.005700 | 7.006800 | 9.720200 | 168.410125 | 47.727850 | 45.169475 | 4.741800 | 23.737200 | 6.703800 | 0.000000 | 3.625300 | 93.119600 | 0.000000 | 514.585900 | 262.865250 | 294.667050 | 288.893450 | 501.607450 | 397.506100 | 510.647150 | 377.144200 | 61.494725 | 2.839800 | 9.078900 | 3.199100 | 0.563000 | 7.897200 | 32.438475 | 14.469050 | 0.000000 | 536.204600 | 505.401000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 64.248700 | 555.294100 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.264900 | 1.759700 | 1.932800 | 10.971850 | 2.903700 | 0.000000 | 17.808950 | 0.133200 | 6.450650 | 6.329500 | 2.211650 | 7.594250 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.947000 | 2.458350 | 11.238400 | 0.113400 | 0.009000 | 0.002600 | 8.020700 | 1.285550 | 407.431000 | 78.397000 | 0.850350 | 19.035000 | 1.370000 | 0.358450 | 8.637150 | 0.562350 | 74.628700 | 4.702700 | 1.815600 | 1.000800 | 0.443600 | 0.089000 | 44.113400 | 265.707000 | 0.768875 | 7.800000 | 0.186150 | 3.098725 | 0.075275 | 2.970850 | 24.772175 | 534.356400 | 2.290650 | 10.130000 | 0.366900 | 3.492500 | 0.112150 | 1.902000 | 17.080900 | 0.027700 | 0.020000 | 0.006475 | 116.539150 | 0.502375 | 0.016500 | 0.004100 | 3.295175 | 0.027600 | 0.020300 | 0.006400 | 114.749700 | -1.000000 |
| max | 3356.350000 | 2846.440000 | 2315.266700 | 3715.041700 | 1114.536600 | 100.000000 | 129.252200 | 0.128600 | 1.656400 | 0.074900 | 0.053000 | 0.984800 | 272.045100 | 0.000000 | 19.546500 | 824.927100 | 102.867700 | 0.984800 | 215.597700 | 12.989800 | 1.453400 | 0.000000 | 3656.250000 | 2363.000000 | 14106.000000 | 1.382800 | 2.052800 | 7.658800 | 77.900000 | 3.511100 | 0.285100 | 4.804400 | 105.603800 | 23.345300 | 59.771100 | 94.264100 | 50.165200 | 67.958600 | 88.418800 | 133.389800 | 86.120000 | 37.880000 | 70.000000 | 377.297300 | 11.053000 | 176.313600 | 789.752300 | 1.511100 | 163.250900 | 1.000000 | 667.741800 | 258.543200 | 0.000000 | 4.764000 | 5.011000 | 2936.000000 | 0.937800 | 0.959800 | 4.847500 | 168.145500 | 373.866400 | 11.784900 | 287.150900 | 188.092300 | 48.988200 | 118.083600 | 770.608400 | 7272.828300 | 167.830900 | 1.000000 | 722.601800 | 238.477500 | 175.413200 | 692.425600 | 4.195500 | 0.231500 | 0.072300 | 0.133100 | 0.249200 | 0.101300 | 0.118600 | 0.058400 | 0.143700 | 8.990400 | 0.150500 | 0.118400 | 2.555500 | 0.993500 | 2105.182300 | 1.472700 | 10746.600000 | 0.362700 | 0.028100 | 0.013300 | 0.001100 | 0.000900 | 2.509300 | 0.000000 | 2.569800 | 0.885400 | 0.002300 | 0.001700 | 0.297900 | 0.020300 | 0.007100 | 0.012700 | 0.017200 | 0.485600 | 0.393800 | 0.984200 | 106.922700 | 236.954600 | 0.488500 | 0.976300 | 0.041400 | 924.531800 | 0.992400 | 311.734400 | 0.624500 | 0.982700 | 7.522000 | 16.070000 | 6.889000 | 16.100000 | 16.100000 | 2.465000 | 3.991000 | 1.175000 | 3.895000 | 2.458000 | 0.888400 | 1.019000 | 2.472300 | 1020.994400 | 64.128700 | 994.000000 | 295.800000 | 334.700000 | 141.799800 | 1770.690900 | 9998.894400 | 0.000000 | 103.390000 | 0.012100 | 0.625300 | 0.250700 | 0.247900 | 0.978300 | 742.942100 | 0.000000 | 22.318000 | 536.564000 | 924.378000 | 0.238900 | 191.547800 | 12.710000 | 2.201600 | 0.287600 | 2505.299800 | 7791.000000 | 4170.000000 | 37943.000000 | 36871.000000 | 0.957000 | 1.817000 | 3.286000 | 21.100000 | 16.300000 | 0.725000 | 1.143000 | 1.153000 | 0.494000 | 0.548400 | 0.864300 | 0.548400 | 1.172000 | 0.441100 | 1.858000 | 0.000000 | 0.000000 | 48.670000 | 3.573000 | 55.000000 | 72.947000 | 3.228300 | 267.910000 | 0.000000 | 307.930000 | 191.830000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.838000 | 396.110000 | 252.870000 | 10.017000 | 390.120000 | 199.620000 | 126.530000 | 490.561000 | 500.349000 | 9998.448300 | 320.050000 | 2.000000 | 457.650000 | 172.349000 | 46.150000 | 0.516400 | 0.322700 | 0.594100 | 1.283700 | 0.761500 | 0.342900 | 0.282800 | 0.674400 | 8.801500 | 0.016300 | 0.024000 | 0.230500 | 0.991100 | 1768.880200 | 1.436100 | 3601.299800 | 0.000000 | 0.154100 | 0.213300 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.024400 | 0.023600 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.984400 | 99.902200 | 237.183700 | 0.491400 | 0.973200 | 0.413800 | 1119.704200 | 0.990900 | 2549.988500 | 0.451700 | 0.078700 | 0.925500 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.157800 | 40.855000 | 10.152900 | 158.526000 | 132.647900 | 122.117400 | 43.573700 | 659.169600 | 3332.596400 | 0.000000 | 32.170900 | 0.003400 | 0.188400 | 0.075500 | 0.059700 | 0.308300 | 232.804900 | 0.000000 | 6.869800 | 207.016100 | 292.227400 | 0.074900 | 59.518700 | 4.420300 | 0.691500 | 0.083100 | 879.226000 | 3933.755000 | 2005.874400 | 15559.952500 | 18520.468300 | 0.526400 | 1.031200 | 1.812300 | 5.711000 | 5.154900 | 0.225800 | 0.333700 | 0.475000 | 0.224600 | 0.211200 | 0.323900 | 0.211200 | 0.443800 | 0.178400 | 0.754900 | 0.000000 | 0.000000 | 0.000000 | 13.095800 | 1.003400 | 15.893400 | 20.045500 | 0.947400 | 79.151500 | 0.000000 | 89.191700 | 51.867800 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.095900 | 174.894400 | 90.515900 | 3.412500 | 172.711900 | 214.862800 | 38.899500 | 196.688000 | 197.498800 | 5043.878900 | 97.708900 | 0.447200 | 156.336000 | 59.324100 | 257.010600 | 187.758900 | 13.914700 | 0.220000 | 0.133900 | 0.291400 | 0.618800 | 0.142900 | 0.153500 | 0.134400 | 0.278900 | 2.834800 | 0.005200 | 0.004700 | 0.088800 | 0.409000 | 547.172200 | 0.416300 | 1072.203100 | 0.000000 | 0.036800 | 0.039200 | 0.035700 | 0.033400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.008200 | 0.007700 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.627100 | 30.998200 | 74.844500 | 0.207300 | 0.306800 | 0.130900 | 348.829300 | 0.312700 | 805.393600 | 0.137500 | 0.022900 | 0.299400 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.051400 | 14.727700 | 3.312800 | 44.310000 | 9.576500 | 13.807100 | 6.215000 | 128.281600 | 899.119000 | 0.000000 | 116.861500 | 9.690000 | 39.037600 | 999.316000 | 998.681300 | 111.495600 | 273.095200 | 0.000000 | 424.215200 | 103.180900 | 898.608500 | 24.990400 | 113.223000 | 118.753300 | 186.616400 | 400.000000 | 400.000000 | 994.285700 | 995.744700 | 400.000000 | 400.000000 | 400.000000 | 32.274000 | 851.612900 | 657.762100 | 33.058000 | 1.277100 | 5.131700 | 1.085100 | 1.351100 | 1.108700 | 1.763900 | 0.508500 | 1.475400 | 0.000000 | 0.000000 | 0.000000 | 13.977600 | 34.490200 | 42.070300 | 10.184000 | 232.125800 | 164.109300 | 0.000000 | 47.777200 | 149.385100 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 109.007400 | 999.877000 | 77.800700 | 87.134700 | 212.655700 | 492.771800 | 358.950400 | 415.435500 | 79.116200 | 274.887100 | 289.826400 | 200.000000 | 63.333600 | 221.974700 | 0.000000 | 999.413500 | 989.473700 | 996.858600 | 994.000000 | 999.491100 | 995.744700 | 997.518600 | 994.003500 | 142.843600 | 12.769800 | 21.044300 | 9.402400 | 127.572800 | 107.692600 | 219.643600 | 40.281800 | 0.000000 | 1000.000000 | 999.233700 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 451.485100 | 1000.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 252.860400 | 113.275800 | 111.349500 | 184.348800 | 111.736500 | 1000.000000 | 137.983800 | 111.333000 | 818.000500 | 80.040600 | 8.203700 | 14.447900 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.580300 | 4.082500 | 25.779200 | 0.118400 | 0.024000 | 0.004700 | 21.044300 | 3.978600 | 421.702000 | 83.720000 | 7.065600 | 131.680000 | 39.330000 | 2.718200 | 56.930300 | 17.478100 | 303.550000 | 35.319800 | 54.291700 | 1.512100 | 1.073700 | 0.445700 | 101.114600 | 311.404000 | 1.298800 | 32.580000 | 0.689200 | 14.014100 | 0.293200 | 12.746200 | 84.802400 | 589.508200 | 2.739500 | 454.560000 | 2.196700 | 170.020400 | 0.550200 | 90.423500 | 96.960100 | 0.102800 | 0.079900 | 0.028600 | 737.304800 | 0.509800 | 0.476600 | 0.104500 | 99.303200 | 0.102800 | 0.079900 | 0.028600 | 737.304800 | 1.000000 |
signal_df['Pass/Fail'].value_counts()
-1 1463 1 104 Name: Pass/Fail, dtype: int64
signal_df.isnull().sum().sum()
41951
signal_df = signal_df.drop('Time', axis=1)
for col in signal_df.columns:
if signal_df[col].isnull().sum() / len(signal_df) >= 0.2:
print(col)
signal_df.drop(col, axis=1, inplace=True)
else:
signal_df[col].fillna(signal_df[col].mean(), inplace=True)
72 73 85 109 110 111 112 157 158 220 244 245 246 247 292 293 345 346 358 382 383 384 385 492 516 517 518 519 578 579 580 581
signal_df.isnull().sum().sum()
0
signal_df.shape
(1567, 559)
# Here we are using the nunique fucntion to identify the number of unique values in the columns.
#The columns which has only one Unique value is being dropped
nunique = signal_df.nunique()
cols_to_drop = nunique[nunique == 1].index
signal_df.drop(cols_to_drop, axis=1,inplace=True)
print(signal_df.shape)
(1567, 443)
signal_var = signal_df.drop('Pass/Fail', 1).var().round(2)
low_var_features = signal_var[signal_var <= 0.1]
low_var_features
7 0.00
8 0.01
9 0.00
10 0.00
11 0.00
...
583 0.00
584 0.00
586 0.00
587 0.00
588 0.00
Length: 188, dtype: float64
signal_lvr = signal_df.drop(np.unique(low_var_features.index),axis=1)
signal_lvr.shape
(1567, 255)
signal_lvr.head(10)
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | ... | 568 | 569 | 570 | 572 | 574 | 576 | 577 | 585 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 97.6133 | 202.4396 | 7.9558 | 414.8710 | 10.0433 | ... | 2.452417 | 21.117674 | 533.8500 | 8.95 | 3.0624 | 1.6765 | 14.9509 | 2.3630 | 99.670066 | -1 |
| 1 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 200.5470 | 10.1548 | 414.7347 | 9.2599 | ... | 2.452417 | 21.117674 | 535.0164 | 5.92 | 2.0111 | 1.1065 | 10.9003 | 4.4447 | 208.204500 | -1 |
| 2 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 95.4878 | 202.0179 | 9.5157 | 416.7075 | 9.3144 | ... | 0.411900 | 68.848900 | 535.0245 | 11.21 | 4.0923 | 2.0952 | 9.2721 | 3.1745 | 82.860200 | 1 |
| 3 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 104.2367 | 201.8482 | 9.6052 | 422.2894 | 9.6924 | ... | 2.729000 | 25.036300 | 530.5682 | 9.33 | 2.8971 | 1.7585 | 8.5831 | 2.0544 | 73.843200 | -1 |
| 4 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 201.9424 | 10.5661 | 420.5925 | 10.3387 | ... | 2.452417 | 21.117674 | 532.0155 | 8.83 | 3.1776 | 1.6597 | 10.9698 | 99.3032 | 73.843200 | -1 |
| 5 | 2946.25 | 2432.84 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 200.4720 | 8.6617 | 414.2426 | 9.2441 | ... | 1.870000 | 22.559800 | 534.2091 | 8.91 | 2.2598 | 1.6679 | 13.7755 | 3.8276 | 44.007700 | -1 |
| 6 | 3030.27 | 2430.12 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 202.0901 | 9.0350 | 415.8852 | 9.9990 | ... | 2.452417 | 21.117674 | 541.9036 | 6.48 | 2.2019 | 1.1958 | 8.3645 | 2.8515 | 44.007700 | -1 |
| 7 | 3058.88 | 2690.15 | 2248.9000 | 1004.4692 | 0.7884 | 106.2400 | 202.4170 | 13.6872 | 408.4017 | 9.6836 | ... | 2.934900 | 23.605200 | 493.0054 | 278.19 | 92.5866 | 56.4274 | 16.0862 | 2.1261 | 95.031000 | -1 |
| 8 | 2967.68 | 2600.47 | 2248.9000 | 1004.4692 | 0.7884 | 106.2400 | 202.4544 | 12.6837 | 417.6009 | 9.7046 | ... | 2.368200 | 18.212000 | 535.1818 | 7.09 | 2.4902 | 1.3248 | 14.2892 | 3.4456 | 111.652500 | -1 |
| 9 | 3016.11 | 2428.37 | 2248.9000 | 1004.4692 | 0.7884 | 106.2400 | 202.5999 | 12.4278 | 413.3677 | 9.7046 | ... | 2.654500 | 5.861700 | 533.4200 | 3.54 | 1.0395 | 0.6636 | 7.4181 | 3.0687 | 90.229400 | -1 |
10 rows × 255 columns
sns.heatmap(signal_lvr.corr(), center=0)
<Axes: >
corel_mat=signal_lvr.corr()
corel_mat.head(10)
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | ... | 568 | 569 | 570 | 572 | 574 | 576 | 577 | 585 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.000000 | -0.143840 | 0.004756 | -0.007613 | -0.011014 | 0.002270 | 0.010368 | -0.007058 | 0.030675 | -0.005749 | ... | 0.060010 | 0.049862 | -0.018953 | 0.013678 | 0.015206 | 0.013228 | 0.008601 | 0.023589 | 0.004174 | -0.025141 |
| 1 | -0.143840 | 1.000000 | 0.005767 | -0.007568 | -0.001636 | -0.025564 | 0.034062 | -0.037667 | -0.087315 | -0.001878 | ... | -0.017051 | -0.025490 | -0.009000 | 0.001753 | 0.001303 | 0.002570 | -0.010145 | 0.002273 | 0.044797 | -0.002603 |
| 2 | 0.004756 | 0.005767 | 1.000000 | 0.298935 | 0.095891 | -0.136225 | 0.018326 | 0.006476 | 0.006115 | -0.000788 | ... | 0.050434 | 0.064282 | -0.037070 | -0.000518 | 0.001342 | 0.002592 | -0.028705 | 0.015752 | -0.032890 | -0.000957 |
| 3 | -0.007613 | -0.007568 | 0.298935 | 1.000000 | -0.058483 | -0.685835 | -0.028223 | -0.019827 | -0.013157 | -0.004596 | ... | 0.008646 | 0.046434 | 0.002231 | 0.007634 | 0.006822 | 0.008216 | 0.016438 | 0.026019 | -0.080341 | -0.024623 |
| 4 | -0.011014 | -0.001636 | 0.095891 | -0.058483 | 1.000000 | -0.074368 | -0.002707 | -0.017523 | 0.011435 | -0.001763 | ... | -0.012944 | 0.027696 | 0.005273 | -0.012024 | -0.012264 | -0.012163 | -0.004070 | -0.001616 | 0.050910 | -0.013756 |
| 6 | 0.002270 | -0.025564 | -0.136225 | -0.685835 | -0.074368 | 1.000000 | 0.058982 | 0.055333 | 0.039815 | 0.040015 | ... | 0.016000 | -0.070722 | 0.017264 | 0.009292 | 0.007783 | 0.007409 | -0.012342 | -0.039517 | 0.043777 | 0.016239 |
| 12 | 0.010368 | 0.034062 | 0.018326 | -0.028223 | -0.002707 | 0.058982 | 1.000000 | -0.012805 | -0.033933 | 0.552542 | ... | 0.003572 | -0.007246 | -0.000639 | 0.036757 | 0.032908 | 0.035743 | 0.031434 | 0.000523 | -0.036720 | -0.005969 |
| 14 | -0.007058 | -0.037667 | 0.006476 | -0.019827 | -0.017523 | 0.055333 | -0.012805 | 1.000000 | 0.133463 | -0.015420 | ... | -0.008181 | 0.002705 | 0.020110 | -0.000807 | 0.000409 | -0.000985 | 0.009505 | 0.002535 | 0.068161 | -0.068975 |
| 15 | 0.030675 | -0.087315 | 0.006115 | -0.013157 | 0.011435 | 0.039815 | -0.033933 | 0.133463 | 1.000000 | -0.020408 | ... | 0.014993 | 0.000864 | 0.011928 | -0.024789 | -0.024032 | -0.023509 | -0.019152 | 0.017745 | 0.009764 | -0.002884 |
| 16 | -0.005749 | -0.001878 | -0.000788 | -0.004596 | -0.001763 | 0.040015 | 0.552542 | -0.015420 | -0.020408 | 1.000000 | ... | -0.007759 | -0.009876 | 0.007679 | -0.014068 | -0.014005 | -0.014167 | -0.004396 | 0.002643 | -0.013918 | 0.002356 |
10 rows × 255 columns
upper = corel_mat.where(np.triu(np.ones(corel_mat.shape), k=1).astype(np.bool))
upper.head(10)
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | ... | 568 | 569 | 570 | 572 | 574 | 576 | 577 | 585 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | -0.14384 | 0.004756 | -0.007613 | -0.011014 | 0.002270 | 0.010368 | -0.007058 | 0.030675 | -0.005749 | ... | 0.060010 | 0.049862 | -0.018953 | 0.013678 | 0.015206 | 0.013228 | 0.008601 | 0.023589 | 0.004174 | -0.025141 |
| 1 | NaN | NaN | 0.005767 | -0.007568 | -0.001636 | -0.025564 | 0.034062 | -0.037667 | -0.087315 | -0.001878 | ... | -0.017051 | -0.025490 | -0.009000 | 0.001753 | 0.001303 | 0.002570 | -0.010145 | 0.002273 | 0.044797 | -0.002603 |
| 2 | NaN | NaN | NaN | 0.298935 | 0.095891 | -0.136225 | 0.018326 | 0.006476 | 0.006115 | -0.000788 | ... | 0.050434 | 0.064282 | -0.037070 | -0.000518 | 0.001342 | 0.002592 | -0.028705 | 0.015752 | -0.032890 | -0.000957 |
| 3 | NaN | NaN | NaN | NaN | -0.058483 | -0.685835 | -0.028223 | -0.019827 | -0.013157 | -0.004596 | ... | 0.008646 | 0.046434 | 0.002231 | 0.007634 | 0.006822 | 0.008216 | 0.016438 | 0.026019 | -0.080341 | -0.024623 |
| 4 | NaN | NaN | NaN | NaN | NaN | -0.074368 | -0.002707 | -0.017523 | 0.011435 | -0.001763 | ... | -0.012944 | 0.027696 | 0.005273 | -0.012024 | -0.012264 | -0.012163 | -0.004070 | -0.001616 | 0.050910 | -0.013756 |
| 6 | NaN | NaN | NaN | NaN | NaN | NaN | 0.058982 | 0.055333 | 0.039815 | 0.040015 | ... | 0.016000 | -0.070722 | 0.017264 | 0.009292 | 0.007783 | 0.007409 | -0.012342 | -0.039517 | 0.043777 | 0.016239 |
| 12 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.012805 | -0.033933 | 0.552542 | ... | 0.003572 | -0.007246 | -0.000639 | 0.036757 | 0.032908 | 0.035743 | 0.031434 | 0.000523 | -0.036720 | -0.005969 |
| 14 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.133463 | -0.015420 | ... | -0.008181 | 0.002705 | 0.020110 | -0.000807 | 0.000409 | -0.000985 | 0.009505 | 0.002535 | 0.068161 | -0.068975 |
| 15 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.020408 | ... | 0.014993 | 0.000864 | 0.011928 | -0.024789 | -0.024032 | -0.023509 | -0.019152 | 0.017745 | 0.009764 | -0.002884 |
| 16 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | -0.007759 | -0.009876 | 0.007679 | -0.014068 | -0.014005 | -0.014167 | -0.004396 | 0.002643 | -0.013918 | 0.002356 |
10 rows × 255 columns
hi_col=[column for column in upper.columns if any(upper[column] >.9)]
print("Columns having corelation greater than .9 which is industry standard",len(hi_col))
Columns having corelation greater than .9 which is industry standard 95
signal_new = signal_lvr.drop(columns = hi_col)
signal_new.shape
(1567, 160)
signal_new.head(10)
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | ... | 561 | 562 | 564 | 569 | 570 | 572 | 577 | 585 | 589 | Pass/Fail | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 97.6133 | 202.4396 | 7.9558 | 414.8710 | 10.0433 | ... | 42.3877 | 262.729683 | 6.444985 | 21.117674 | 533.8500 | 8.95 | 14.9509 | 2.3630 | 99.670066 | -1 |
| 1 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 200.5470 | 10.1548 | 414.7347 | 9.2599 | ... | 18.1087 | 262.729683 | 6.444985 | 21.117674 | 535.0164 | 5.92 | 10.9003 | 4.4447 | 208.204500 | -1 |
| 2 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 95.4878 | 202.0179 | 9.5157 | 416.7075 | 9.3144 | ... | 24.7524 | 267.064000 | 1.100000 | 68.848900 | 535.0245 | 11.21 | 9.2721 | 3.1745 | 82.860200 | 1 |
| 3 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 104.2367 | 201.8482 | 9.6052 | 422.2894 | 9.6924 | ... | 62.7572 | 268.228000 | 7.320000 | 25.036300 | 530.5682 | 9.33 | 8.5831 | 2.0544 | 73.843200 | -1 |
| 4 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 201.9424 | 10.5661 | 420.5925 | 10.3387 | ... | 22.0500 | 262.729683 | 6.444985 | 21.117674 | 532.0155 | 8.83 | 10.9698 | 99.3032 | 73.843200 | -1 |
| 5 | 2946.25 | 2432.84 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 200.4720 | 8.6617 | 414.2426 | 9.2441 | ... | 30.6277 | 254.006000 | 4.750000 | 22.559800 | 534.2091 | 8.91 | 13.7755 | 3.8276 | 44.007700 | -1 |
| 6 | 3030.27 | 2430.12 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 202.0901 | 9.0350 | 415.8852 | 9.9990 | ... | 51.4535 | 262.729683 | 6.444985 | 21.117674 | 541.9036 | 6.48 | 8.3645 | 2.8515 | 44.007700 | -1 |
| 7 | 3058.88 | 2690.15 | 2248.9000 | 1004.4692 | 0.7884 | 106.2400 | 202.4170 | 13.6872 | 408.4017 | 9.6836 | ... | 43.0771 | 265.090000 | 7.780000 | 23.605200 | 493.0054 | 278.19 | 16.0862 | 2.1261 | 95.031000 | -1 |
| 8 | 2967.68 | 2600.47 | 2248.9000 | 1004.4692 | 0.7884 | 106.2400 | 202.4544 | 12.6837 | 417.6009 | 9.7046 | ... | 13.9158 | 265.184000 | 6.280000 | 18.212000 | 535.1818 | 7.09 | 14.2892 | 3.4456 | 111.652500 | -1 |
| 9 | 3016.11 | 2428.37 | 2248.9000 | 1004.4692 | 0.7884 | 106.2400 | 202.5999 | 12.4278 | 413.3677 | 9.7046 | ... | 20.9776 | 265.206000 | 7.040000 | 5.861700 | 533.4200 | 3.54 | 7.4181 | 3.0687 | 90.229400 | -1 |
10 rows × 160 columns
#finding unique values in each columns
df = signal_new.drop('Pass/Fail', 1).nunique()
#finding columns having less than 10 unique values
Drop = df[df <= 10]
print("Features having less than 10 unique values \n", Drop)
Features having less than 10 unique values 209 3 521 9 dtype: int64
signal_new=signal_new.drop(np.unique(Drop.index),axis=1)
signal_new.shape
(1567, 158)
columns = list(signal_new.iloc[:,1:158])
signal_new[columns].hist(stacked=True, bins=100, figsize=(18,30), layout=(20,10));
sns.countplot(data= signal_new, x= signal_new['Pass/Fail'])
<Axes: xlabel='Pass/Fail', ylabel='count'>
signal_new['Pass/Fail'].value_counts(normalize=True)
-1 0.933631 1 0.066369 Name: Pass/Fail, dtype: float64
Univariate analysis of all the predictors features:
Univariate analysis of all the Target features:
plt.figure(figsize=(20, 50))
col = 1
for i in signal_new.columns:
plt.subplot(56,10, col)
sns.boxplot(signal_new[i],color='blue',orient='h')
col += 1
sns.pairplot(signal_new.iloc[:,0:15]);
sns.pairplot(signal_new.iloc[:,16:30]);
plt.rcParams['figure.figsize'] = (18, 18)
sns.heatmap(signal_new.corr(), cmap = "viridis")
plt.title('Correlation heatmap for the Data', fontsize = 20)
Text(0.5, 1.0, 'Correlation heatmap for the Data')
multivariate analysis :
signal_new.columns
Index(['0', '1', '2', '3', '4', '6', '12', '14', '15', '16',
...
'561', '562', '564', '569', '570', '572', '577', '585', '589',
'Pass/Fail'],
dtype='object', length=158)
X = signal_new.drop('Pass/Fail', axis=1)
y = signal_new['Pass/Fail']
X.head()
| 0 | 1 | 2 | 3 | 4 | 6 | 12 | 14 | 15 | 16 | ... | 555 | 561 | 562 | 564 | 569 | 570 | 572 | 577 | 585 | 589 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3030.93 | 2564.00 | 2187.7333 | 1411.1265 | 1.3602 | 97.6133 | 202.4396 | 7.9558 | 414.8710 | 10.0433 | ... | 39.8842 | 42.3877 | 262.729683 | 6.444985 | 21.117674 | 533.8500 | 8.95 | 14.9509 | 2.3630 | 99.670066 |
| 1 | 3095.78 | 2465.14 | 2230.4222 | 1463.6606 | 0.8294 | 102.3433 | 200.5470 | 10.1548 | 414.7347 | 9.2599 | ... | 53.1836 | 18.1087 | 262.729683 | 6.444985 | 21.117674 | 535.0164 | 5.92 | 10.9003 | 4.4447 | 208.204500 |
| 2 | 2932.61 | 2559.94 | 2186.4111 | 1698.0172 | 1.5102 | 95.4878 | 202.0179 | 9.5157 | 416.7075 | 9.3144 | ... | 23.0713 | 24.7524 | 267.064000 | 1.100000 | 68.848900 | 535.0245 | 11.21 | 9.2721 | 3.1745 | 82.860200 |
| 3 | 2988.72 | 2479.90 | 2199.0333 | 909.7926 | 1.3204 | 104.2367 | 201.8482 | 9.6052 | 422.2894 | 9.6924 | ... | 161.4081 | 62.7572 | 268.228000 | 7.320000 | 25.036300 | 530.5682 | 9.33 | 8.5831 | 2.0544 | 73.843200 |
| 4 | 3032.24 | 2502.87 | 2233.3667 | 1326.5200 | 1.5334 | 100.3967 | 201.9424 | 10.5661 | 420.5925 | 10.3387 | ... | 70.9706 | 22.0500 | 262.729683 | 6.444985 | 21.117674 | 532.0155 | 8.83 | 10.9698 | 99.3032 | 73.843200 |
5 rows × 157 columns
y.head()
0 -1 1 -1 2 1 3 -1 4 -1 Name: Pass/Fail, dtype: int64
y.value_counts(normalize=True)
-1 0.933631 1 0.066369 Name: Pass/Fail, dtype: float64
from imblearn.over_sampling import SMOTE
smote= SMOTE()
X_os, y_os= smote.fit_resample(X, y)
print(X.shape)
print(y.shape)
print(X_os.shape)
print(y_os.shape)
(1567, 157) (1567,) (2926, 157) (2926,)
y_os.value_counts()
-1 1463 1 1463 Name: Pass/Fail, dtype: int64
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X_os, y_os, test_size=0.25, stratify = y_os, random_state=101)
print("x_train shape",x_train.shape)
print("x_test shape",x_test.shape)
print("y_train shape",y_train.shape)
print("y_test shape",y_test.shape)
x_train shape (2194, 157) x_test shape (732, 157) y_train shape (2194,) y_test shape (732,)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
col=X.columns
x_train
array([[-0.14529223, -0.65771275, 0.64506089, ..., -0.56907992,
-0.1944015 , 0.52811401],
[-1.62119751, 0.3236986 , -1.34099509, ..., -0.67282274,
-0.87604016, 1.24647417],
[ 0.50428457, -1.11702265, 0.11176889, ..., -0.06269127,
-0.94149319, -0.86634674],
...,
[ 0.08698154, 0.53864555, 0.65602362, ..., -0.33184925,
-0.98685128, -1.20044503],
[-0.14228397, 2.84989909, -0.85809845, ..., 0.63067931,
-0.44228299, 2.01506081],
[ 1.14568173, 0.6517258 , -2.3284581 , ..., 0.43199958,
-1.00011731, -0.47705725]])
x_test
array([[ 0.32398039, -0.35310065, 0.27792146, ..., -0.13705271,
0.86628221, -0.76533208],
[-0.07458028, 0.1648313 , 0.79132291, ..., -0.26216665,
-0.86950732, -0.4269443 ],
[-1.72660119, 0.36235153, 1.94870764, ..., -0.22734821,
0.09258193, -0.72931933],
...,
[-0.53128696, 0.44024197, 1.3160396 , ..., 0.12547964,
1.03094982, -0.70503283],
[ 0.43179597, -0.64932608, 1.69539635, ..., -0.22910387,
-0.87155394, 0.18346181],
[-0.13388346, -0.52787645, 0.42006061, ..., -0.26634893,
-0.23064968, 0.10536855]])
from sklearn.tree import DecisionTreeClassifier
dTree = DecisionTreeClassifier(criterion = 'gini', random_state=101)
from sklearn.metrics import accuracy_score, fbeta_score, make_scorer
dTree.fit(x_train, y_train)
DecisionTreeClassifier(random_state=101)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(random_state=101)
print("Train score",dTree.score(x_train,y_train))
print("Test score",dTree.score(x_test,y_test))
Train score 1.0 Test score 0.8797814207650273
Hint: Use all CV techniques that you have learnt in the course.
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV,KFold, cross_val_score
parameters = {'max_depth': range(1, 20),'criterion': ['gini', 'entropy'],'min_samples_leaf':range(2, 10)}
scorer = make_scorer(fbeta_score, beta=0.5)
Using Gridsearch CV for decison tree
dt_classifier= GridSearchCV(dTree,param_grid=parameters,scoring=scorer)
grid_fit = dt_classifier.fit(x_train, y_train)
best_clf = grid_fit.best_estimator_
best_predictions = best_clf.predict(x_test)
dt_gs_result=pd.DataFrame(grid_fit.cv_results_)
dt_gs_result
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_criterion | param_max_depth | param_min_samples_leaf | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.023294 | 0.004027 | 0.000929 | 0.000824 | gini | 1 | 2 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.663943 | 0.737148 | 0.723982 | 0.708175 | 0.678060 | 0.702262 | 0.027488 | 289 |
| 1 | 0.025248 | 0.005607 | 0.000000 | 0.000000 | gini | 1 | 3 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.663943 | 0.737148 | 0.723982 | 0.708175 | 0.678060 | 0.702262 | 0.027488 | 289 |
| 2 | 0.021961 | 0.007750 | 0.000000 | 0.000000 | gini | 1 | 4 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.663943 | 0.737148 | 0.723982 | 0.708175 | 0.678060 | 0.702262 | 0.027488 | 289 |
| 3 | 0.021959 | 0.009410 | 0.003125 | 0.006249 | gini | 1 | 5 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.663943 | 0.737148 | 0.723982 | 0.708175 | 0.678060 | 0.702262 | 0.027488 | 289 |
| 4 | 0.025113 | 0.007746 | 0.000000 | 0.000000 | gini | 1 | 6 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.663943 | 0.737148 | 0.723982 | 0.708175 | 0.678060 | 0.702262 | 0.027488 | 289 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 299 | 0.217685 | 0.023812 | 0.000000 | 0.000000 | entropy | 19 | 5 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.847688 | 0.845557 | 0.859899 | 0.829038 | 0.876242 | 0.851685 | 0.015729 | 52 |
| 300 | 0.212908 | 0.019606 | 0.000000 | 0.000000 | entropy | 19 | 6 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.851819 | 0.817694 | 0.866725 | 0.836268 | 0.847534 | 0.844008 | 0.016382 | 86 |
| 301 | 0.210777 | 0.020718 | 0.000000 | 0.000000 | entropy | 19 | 7 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.829675 | 0.808758 | 0.852076 | 0.817474 | 0.840259 | 0.829648 | 0.015493 | 147 |
| 302 | 0.210576 | 0.021159 | 0.000000 | 0.000000 | entropy | 19 | 8 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.823256 | 0.814581 | 0.847751 | 0.812500 | 0.834106 | 0.826439 | 0.013100 | 178 |
| 303 | 0.207728 | 0.017968 | 0.000000 | 0.000000 | entropy | 19 | 9 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.821791 | 0.811044 | 0.831926 | 0.830292 | 0.840415 | 0.827094 | 0.009967 | 168 |
304 rows × 16 columns
dt_gs_result[['param_criterion','param_max_depth','params','mean_test_score','rank_test_score']]
| param_criterion | param_max_depth | params | mean_test_score | rank_test_score | |
|---|---|---|---|---|---|
| 0 | gini | 1 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.702262 | 289 |
| 1 | gini | 1 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.702262 | 289 |
| 2 | gini | 1 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.702262 | 289 |
| 3 | gini | 1 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.702262 | 289 |
| 4 | gini | 1 | {'criterion': 'gini', 'max_depth': 1, 'min_sam... | 0.702262 | 289 |
| ... | ... | ... | ... | ... | ... |
| 299 | entropy | 19 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.851685 | 52 |
| 300 | entropy | 19 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.844008 | 86 |
| 301 | entropy | 19 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.829648 | 147 |
| 302 | entropy | 19 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.826439 | 178 |
| 303 | entropy | 19 | {'criterion': 'entropy', 'max_depth': 19, 'min... | 0.827094 | 168 |
304 rows × 5 columns
# Evaluate the optimized model
print("Optimized Model:")
print("Best parameters:", grid_fit.best_params_)
print("Final accuracy score on testing data: {:.4f}".format(accuracy_score(y_test, best_predictions)))
print("Final F-score on testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta=0.5)))
Optimized Model:
Best parameters: {'criterion': 'gini', 'max_depth': 17, 'min_samples_leaf': 2}
Final accuracy score on testing data: 0.8743
Final F-score on testing data: 0.8649
Using RandomizedSearchCV for decison tree
dt_classifier= RandomizedSearchCV(dTree,param_distributions=parameters,scoring=scorer)
random_fit = dt_classifier.fit(x_train, y_train)
dt_Rs_result=pd.DataFrame(random_fit.cv_results_)
dt_Rs_result
| mean_fit_time | std_fit_time | mean_score_time | std_score_time | param_min_samples_leaf | param_max_depth | param_criterion | params | split0_test_score | split1_test_score | split2_test_score | split3_test_score | split4_test_score | mean_test_score | std_test_score | rank_test_score | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.190554 | 0.015333 | 0.003526 | 0.006100 | 3 | 19 | gini | {'min_samples_leaf': 3, 'max_depth': 19, 'crit... | 0.831075 | 0.873154 | 0.834760 | 0.867347 | 0.887764 | 0.858820 | 0.022202 | 1 |
| 1 | 0.219806 | 0.013554 | 0.000000 | 0.000000 | 3 | 11 | entropy | {'min_samples_leaf': 3, 'max_depth': 11, 'crit... | 0.839895 | 0.828500 | 0.845960 | 0.830565 | 0.881057 | 0.845195 | 0.019013 | 5 |
| 2 | 0.044263 | 0.006368 | 0.000000 | 0.000000 | 7 | 2 | gini | {'min_samples_leaf': 7, 'max_depth': 2, 'crite... | 0.742138 | 0.737483 | 0.715241 | 0.763221 | 0.728430 | 0.737303 | 0.015879 | 9 |
| 3 | 0.161549 | 0.007537 | 0.000000 | 0.000000 | 5 | 11 | gini | {'min_samples_leaf': 5, 'max_depth': 11, 'crit... | 0.802357 | 0.871314 | 0.820578 | 0.852473 | 0.873965 | 0.844138 | 0.028278 | 6 |
| 4 | 0.179896 | 0.018663 | 0.005418 | 0.006644 | 4 | 19 | gini | {'min_samples_leaf': 4, 'max_depth': 19, 'crit... | 0.818438 | 0.875811 | 0.836364 | 0.866725 | 0.875357 | 0.854539 | 0.023100 | 3 |
| 5 | 0.160784 | 0.014280 | 0.000000 | 0.000000 | 7 | 13 | gini | {'min_samples_leaf': 7, 'max_depth': 13, 'crit... | 0.784225 | 0.849492 | 0.812274 | 0.840444 | 0.847382 | 0.826763 | 0.025093 | 7 |
| 6 | 0.233045 | 0.034323 | 0.002803 | 0.005606 | 2 | 17 | entropy | {'min_samples_leaf': 2, 'max_depth': 17, 'crit... | 0.860692 | 0.852614 | 0.850340 | 0.827815 | 0.885276 | 0.855347 | 0.018520 | 2 |
| 7 | 0.041059 | 0.008720 | 0.000000 | 0.000000 | 5 | 2 | gini | {'min_samples_leaf': 5, 'max_depth': 2, 'crite... | 0.742138 | 0.737483 | 0.715241 | 0.763221 | 0.728430 | 0.737303 | 0.015879 | 9 |
| 8 | 0.217011 | 0.015517 | 0.000000 | 0.000000 | 5 | 18 | entropy | {'min_samples_leaf': 5, 'max_depth': 18, 'crit... | 0.847688 | 0.845557 | 0.859899 | 0.829038 | 0.876242 | 0.851685 | 0.015729 | 4 |
| 9 | 0.097550 | 0.006238 | 0.000000 | 0.000000 | 5 | 5 | gini | {'min_samples_leaf': 5, 'max_depth': 5, 'crite... | 0.763828 | 0.847234 | 0.805085 | 0.791536 | 0.802161 | 0.801969 | 0.026916 | 8 |
dt_Rs_result[['param_max_depth','params','params','rank_test_score','mean_test_score']]
| param_max_depth | params | params | rank_test_score | mean_test_score | |
|---|---|---|---|---|---|
| 0 | 19 | {'min_samples_leaf': 3, 'max_depth': 19, 'crit... | {'min_samples_leaf': 3, 'max_depth': 19, 'crit... | 1 | 0.858820 |
| 1 | 11 | {'min_samples_leaf': 3, 'max_depth': 11, 'crit... | {'min_samples_leaf': 3, 'max_depth': 11, 'crit... | 5 | 0.845195 |
| 2 | 2 | {'min_samples_leaf': 7, 'max_depth': 2, 'crite... | {'min_samples_leaf': 7, 'max_depth': 2, 'crite... | 9 | 0.737303 |
| 3 | 11 | {'min_samples_leaf': 5, 'max_depth': 11, 'crit... | {'min_samples_leaf': 5, 'max_depth': 11, 'crit... | 6 | 0.844138 |
| 4 | 19 | {'min_samples_leaf': 4, 'max_depth': 19, 'crit... | {'min_samples_leaf': 4, 'max_depth': 19, 'crit... | 3 | 0.854539 |
| 5 | 13 | {'min_samples_leaf': 7, 'max_depth': 13, 'crit... | {'min_samples_leaf': 7, 'max_depth': 13, 'crit... | 7 | 0.826763 |
| 6 | 17 | {'min_samples_leaf': 2, 'max_depth': 17, 'crit... | {'min_samples_leaf': 2, 'max_depth': 17, 'crit... | 2 | 0.855347 |
| 7 | 2 | {'min_samples_leaf': 5, 'max_depth': 2, 'crite... | {'min_samples_leaf': 5, 'max_depth': 2, 'crite... | 9 | 0.737303 |
| 8 | 18 | {'min_samples_leaf': 5, 'max_depth': 18, 'crit... | {'min_samples_leaf': 5, 'max_depth': 18, 'crit... | 4 | 0.851685 |
| 9 | 5 | {'min_samples_leaf': 5, 'max_depth': 5, 'crite... | {'min_samples_leaf': 5, 'max_depth': 5, 'crite... | 8 | 0.801969 |
print(random_fit.best_estimator_)
print('\n')
print(random_fit.get_params)
print('\n')
print(random_fit.best_score_)
DecisionTreeClassifier(max_depth=19, min_samples_leaf=3, random_state=101)
<bound method BaseEstimator.get_params of RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=101),
param_distributions={'criterion': ['gini', 'entropy'],
'max_depth': range(1, 20),
'min_samples_leaf': range(2, 10)},
scoring=make_scorer(fbeta_score, beta=0.5))>
0.858820091781461
Suggestion: Use all possible hyper parameter combinations to extract the best accuracies.
Using the Parameters from the Gridserach CV. Below are the best parameters mentioned"
from sklearn.tree import DecisionTreeClassifier
dTree_Gs_bP = DecisionTreeClassifier(criterion = 'gini', max_depth=19,min_samples_leaf=2)
dTree_Gs_bP.fit(x_train, y_train)
DecisionTreeClassifier(max_depth=19, min_samples_leaf=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(max_depth=19, min_samples_leaf=2)
print("Train score",dTree_Gs_bP.score(x_train,y_train))
print("Test score",dTree_Gs_bP.score(x_test,y_test))
Train score 0.9908842297174111 Test score 0.8702185792349727
Using the Parameters from the Randomisedsearch CV. Below are the best parameters mentioned"
dTree_rs_bP = DecisionTreeClassifier(criterion = 'gini', max_depth=11,min_samples_leaf=5,random_state=101)
dTree_rs_bP.fit(x_train, y_train)
DecisionTreeClassifier(max_depth=11, min_samples_leaf=5, random_state=101)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeClassifier(max_depth=11, min_samples_leaf=5, random_state=101)
print("Train score",dTree_rs_bP.score(x_train,y_train))
print("Test score",dTree_rs_bP.score(x_test,y_test))
Train score 0.9685505925250684 Test score 0.8592896174863388
Hint: Dimensionality reduction, attribute removal, standardisation/normalisation, target balancing etc.
from sklearn.decomposition import PCA
pca=PCA()
pca.fit(x_train)
PCA()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PCA()
print("\nPercentage of variation explained by each eigen Vector\n",pca.explained_variance_ratio_)
print("\n Cumulative Variance Explained\n",np.cumsum(pca.explained_variance_ratio_))
Percentage of variation explained by each eigen Vector [6.11427533e-02 5.47323240e-02 3.72684688e-02 3.41549409e-02 2.93779944e-02 2.60567719e-02 2.31273499e-02 2.09534246e-02 1.92905770e-02 1.77060911e-02 1.63519053e-02 1.60139245e-02 1.53352485e-02 1.52376893e-02 1.48552854e-02 1.47570501e-02 1.41876979e-02 1.36688074e-02 1.34899460e-02 1.30907104e-02 1.25244629e-02 1.18818901e-02 1.13981361e-02 1.11311322e-02 1.09433411e-02 1.07765798e-02 1.05494680e-02 1.02908850e-02 1.00312947e-02 9.98417348e-03 9.62885657e-03 9.45975021e-03 9.34281143e-03 8.92632873e-03 8.61157630e-03 8.49134486e-03 8.33887815e-03 8.22624946e-03 7.97175369e-03 7.91111745e-03 7.68768467e-03 7.49103933e-03 7.41748502e-03 7.14674882e-03 7.13024133e-03 6.98082591e-03 6.81926957e-03 6.68853940e-03 6.59767380e-03 6.47299742e-03 6.24119294e-03 6.16529170e-03 6.06738497e-03 5.93388565e-03 5.81132423e-03 5.75017364e-03 5.67451062e-03 5.63036151e-03 5.55294881e-03 5.48101990e-03 5.37705062e-03 5.34321760e-03 5.17453248e-03 5.09449610e-03 5.04215734e-03 4.94820700e-03 4.83930133e-03 4.76592962e-03 4.75069227e-03 4.66623920e-03 4.61924729e-03 4.42149596e-03 4.35366710e-03 4.31260460e-03 4.30101798e-03 4.22670136e-03 4.16734467e-03 4.06984834e-03 3.98321512e-03 3.89991424e-03 3.81931581e-03 3.72481939e-03 3.56525333e-03 3.46227934e-03 3.40524215e-03 3.33770902e-03 3.29342826e-03 3.24316593e-03 3.21979720e-03 3.15864532e-03 3.08070667e-03 2.98049513e-03 2.94547701e-03 2.84732652e-03 2.79828710e-03 2.72320382e-03 2.68235637e-03 2.62609770e-03 2.57192993e-03 2.47762689e-03 2.40529598e-03 2.38004646e-03 2.35373687e-03 2.29144476e-03 2.12152724e-03 2.02046085e-03 1.97786044e-03 1.88937336e-03 1.80470990e-03 1.71094723e-03 1.63287124e-03 1.53626237e-03 1.51618925e-03 1.47346137e-03 1.45553340e-03 1.42720369e-03 1.31491427e-03 1.28286615e-03 1.25639791e-03 1.18520129e-03 1.17068851e-03 1.10845193e-03 1.01532753e-03 9.73307745e-04 9.44131285e-04 7.78311860e-04 7.50182973e-04 7.20667736e-04 6.85591312e-04 6.60873836e-04 6.19061767e-04 5.25089242e-04 4.88373774e-04 4.74799088e-04 4.37882914e-04 4.21418527e-04 3.40964001e-04 3.29793641e-04 3.15308940e-04 2.85187580e-04 2.51420251e-04 2.35142673e-04 1.93530410e-04 1.75326261e-04 1.58478881e-04 1.47733536e-04 1.29004444e-04 1.23122742e-04 8.32517717e-05 5.80798873e-05 3.68342563e-05 2.71860295e-05 1.06940680e-05 3.72555383e-06 2.99242644e-06 2.12508291e-06 1.27036229e-12] Cumulative Variance Explained [0.06114275 0.11587508 0.15314355 0.18729849 0.21667648 0.24273325 0.2658606 0.28681403 0.3061046 0.3238107 0.3401626 0.35617653 0.37151177 0.38674946 0.40160475 0.4163618 0.4305495 0.4442183 0.45770825 0.47079896 0.48332342 0.49520531 0.50660345 0.51773458 0.52867792 0.5394545 0.55000397 0.56029486 0.57032615 0.58031032 0.58993918 0.59939893 0.60874174 0.61766807 0.62627965 0.63477099 0.64310987 0.65133612 0.65930787 0.66721899 0.67490668 0.68239772 0.6898152 0.69696195 0.70409219 0.71107302 0.71789229 0.72458083 0.7311785 0.7376515 0.74389269 0.75005798 0.75612537 0.76205925 0.76787058 0.77362075 0.77929526 0.78492562 0.79047857 0.79595959 0.80133664 0.80667986 0.81185439 0.81694889 0.82199104 0.82693925 0.83177855 0.83654448 0.84129517 0.84596141 0.85058066 0.85500216 0.85935582 0.86366843 0.86796945 0.87219615 0.87636349 0.88043334 0.88441656 0.88831647 0.89213579 0.89586061 0.89942586 0.90288814 0.90629338 0.90963109 0.91292452 0.91616768 0.91938748 0.92254613 0.92562683 0.92860733 0.93155281 0.93440013 0.93719842 0.93992162 0.94260398 0.94523008 0.94780201 0.95027963 0.95268493 0.95506498 0.95741871 0.95971016 0.96183168 0.96385215 0.96583001 0.96771938 0.96952409 0.97123504 0.97286791 0.97440417 0.97592036 0.97739382 0.97884935 0.98027656 0.98159147 0.98287434 0.98413074 0.98531594 0.98648663 0.98759508 0.98861041 0.98958371 0.99052784 0.99130616 0.99205634 0.99277701 0.9934626 0.99412347 0.99474253 0.99526762 0.995756 0.9962308 0.99666868 0.9970901 0.99743106 0.99776086 0.99807616 0.99836135 0.99861277 0.99884791 0.99904145 0.99921677 0.99937525 0.99952298 0.99965199 0.99977511 0.99985836 0.99991644 0.99995328 0.99998046 0.99999116 0.99999488 0.99999787 1. 1. ]
'PLOT - CUMULATIVE VARIATON EXPLAINED vs EIGEN VALUE'
cum_val=np.cumsum(pca.explained_variance_ratio_)
plt.figure(figsize=(15,11));
plt.step(range(1,158),cum_val);
plt.ylabel('Cum of variation explained');
plt.xlabel('eigen Value');
plt.show();
pca=PCA(n_components=120)
pca.fit(x_train)
PCA(n_components=120)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
PCA(n_components=120)
x_trainp=pca.transform(x_train)
x_testp=pca.transform(x_test)
param_grid = {'max_depth':[1,2,3,4,5,6,7,8,9,10,11,12],
'min_samples_leaf':[1,2,3,4,5],
'min_samples_split':[1,2,3,4,5,6]}
grid = GridSearchCV(dTree, param_grid,cv=10)
%time grid.fit(x_trainp, y_train)
print(grid.best_params_)
dTreebest = grid.best_estimator_
y_predict_Grid = dTreebest.predict(x_testp)
CPU times: total: 6min
Wall time: 20min 27s
{'max_depth': 12, 'min_samples_leaf': 1, 'min_samples_split': 1}
print(dTreebest.score(x_trainp,y_train))
print(dTreebest.score(x_testp,y_test))
0.9794895168641751 0.8743169398907104
We have reduced the no. of dimensions from 157 to 120. Here, the performance of the model has improved from 88% to 91%.
from sklearn.metrics import classification_report
print(classification_report(y_test, y_predict_Grid, digits=2))
precision recall f1-score support
-1 0.92 0.82 0.87 366
1 0.84 0.93 0.88 366
accuracy 0.87 732
macro avg 0.88 0.87 0.87 732
weighted avg 0.88 0.87 0.87 732
from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,classification_report
Accu_score=accuracy_score(y_test,y_predict_Grid)*100
recall=(recall_score(y_test,y_predict_Grid)*100)
precision=(precision_score(y_test,y_predict_Grid)*100)
f1score=f1_score(y_test,y_predict_Grid)*100
print("Accuracy of Decision Tree is %0.3f"%Accu_score)
print("Misclassification Rate of Decision Tree Model is %0.3f"%(100- Accu_score))
print("F1-Score of Decision Tree is %0.3f"%f1score)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision)
Accuracy of Decision Tree is 87.432 Misclassification Rate of Decision Tree Model is 12.568 F1-Score of Decision Tree is 88.052 Recall( =TP/(TP+FN)) is 92.623 Precision( =TP/(TP+FP)) is 83.911
from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_predict_Grid)
df_conf_mat = pd.DataFrame(conf_mat,
index=['Actual -1', 'Actual 1'],
columns=['predicted -1', 'predicted 1',])
plt.figure(figsize = (7,5))
sns.heatmap(df_conf_mat,annot=True,fmt='.9g');
resultsDf=pd.DataFrame()
tempResultsDf = pd.DataFrame({'Method':['Decision Tree'],'Train Accuracy': dTreebest.score(x_trainp,y_train),'Test Accuracy': Accu_score,'Recall':recall,'Precision':precision,'F1-Score':f1score})
resultsDf = pd.concat([resultsDf, tempResultsDf], ignore_index = True)
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.97949 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
We have a recall and Precion value of greater than 94% which represents that the model is highly accurate
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression()
lr.fit(x_trainp,y_train)
y_pred=lr.predict(x_testp)
print("Logistical regresiion Train score:",lr.score(x_trainp,y_train))
print("Logistical regresiion Test score:",lr.score(x_testp,y_test))
Logistical regresiion Train score: 0.8751139471285324 Logistical regresiion Test score: 0.8237704918032787
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
kfold = KFold(n_splits=10, random_state=77,shuffle=True)
results = cross_val_score(lr,X, y, cv=kfold,)
print(results)
print(np.mean(abs(results)))
print(results.std())
[0.95541401 0.92993631 0.9044586 0.92356688 0.94904459 0.95541401 0.91719745 0.90384615 0.90384615 0.96153846] 0.9304262616364527 0.022074878838925303
Accu_score2=accuracy_score(y_test,y_pred)*100
recall2=(recall_score(y_test,y_pred)*100)
precision2=(precision_score(y_test,y_pred)*100)
f1score2=(f1_score(y_test,y_pred)*100)
print("Accuracy of Logistic Regression Model is %0.3f"%Accu_score2)
print("Misclassification Rate of Logistic Regression Model is %0.3f"%(100- Accu_score2))
print("F!-score of Logistic Regression Model is %0.3f"%f1score2)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall2)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision2)
Accuracy of Logistic Regression Model is 82.377 Misclassification Rate of Logistic Regression Model is 17.623 F!-score of Logistic Regression Model is 83.004 Recall( =TP/(TP+FN)) is 86.066 Precision( =TP/(TP+FP)) is 80.153
print('CLASSIFICATION REPORT FOR LOGISTIC REGRESSION')
print(classification_report(y_test, y_pred, digits=2))
CLASSIFICATION REPORT FOR LOGISTIC REGRESSION
precision recall f1-score support
-1 0.85 0.79 0.82 366
1 0.80 0.86 0.83 366
accuracy 0.82 732
macro avg 0.83 0.82 0.82 732
weighted avg 0.83 0.82 0.82 732
cm=confusion_matrix(y_test, y_pred)
dcm=pd.DataFrame(cm,index=['Actual_0', 'Actual_1'],columns=['Predicted_0', 'Predicted_1',])
plt.figure(figsize = (7,5))
sns.heatmap(dcm,annot=True,fmt='.9g');
tempResultsDf = pd.DataFrame({'Method':['Logistic Regression'],'Train Accuracy': lr.score(x_trainp,y_train),'Test Accuracy': Accu_score2,'Recall':recall2,'Precision':precision2,'F1-Score':f1score2})
resultsDf = pd.concat([resultsDf, tempResultsDf], ignore_index = True)
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.979490 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
| 1 | Logistic Regression | 0.875114 | 82.377049 | 86.065574 | 80.152672 | 83.003953 |
Model 3 - KNN
from sklearn.neighbors import KNeighborsClassifier
knn=KNeighborsClassifier()
param_grid = {'n_neighbors': [3,5,7,9,11,13,19,21,23,25,27,29],
'metric':['euclidean','manhattan']}
grid = GridSearchCV(knn, param_grid,cv=10)
%time grid.fit(x_trainp, y_train)
print(grid.best_params_)
Kbest = grid.best_estimator_
y_predict_Grid = Kbest.predict(x_testp)
CPU times: total: 1min 5s
Wall time: 11.7 s
{'metric': 'euclidean', 'n_neighbors': 3}
print("KNN Train score:",Kbest.score(x_trainp,y_train))
print("KNN Test score:",Kbest.score(x_testp,y_test))
KNN Train score: 0.7752962625341842 KNN Test score: 0.6680327868852459
Accu_score3=accuracy_score(y_test,y_predict_Grid)*100
recall3=(recall_score(y_test,y_predict_Grid)*100)
precision3=(precision_score(y_test,y_predict_Grid)*100)
f1score3=f1_score(y_test,y_predict_Grid)*100
print("Accuracy of KNN is %0.3f"%Accu_score3)
print("Misclassification Rate of KNN Model is %0.3f"%(100- Accu_score3))
print("F1-Score of KNN is %0.3f"%f1score3)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall3)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision3)
Accuracy of KNN is 66.803 Misclassification Rate of KNN Model is 33.197 F1-Score of KNN is 75.077 Recall( =TP/(TP+FN)) is 100.000 Precision( =TP/(TP+FP)) is 60.099
print('CLASSIFICATION REPORT FOR KNN')
print(classification_report(y_test, y_predict_Grid, digits=2))
CLASSIFICATION REPORT FOR KNN
precision recall f1-score support
-1 1.00 0.34 0.50 366
1 0.60 1.00 0.75 366
accuracy 0.67 732
macro avg 0.80 0.67 0.63 732
weighted avg 0.80 0.67 0.63 732
cm=confusion_matrix(y_test, y_predict_Grid)
dcm=pd.DataFrame(cm,index=['Actual_0', 'Actual_1'],columns=['Predicted_0', 'Predicted_1',])
plt.figure(figsize = (7,5))
sns.heatmap(dcm,annot=True,fmt='.9g');
tempResultsDf = pd.DataFrame({'Method':['KNN'], 'Train Accuracy': '0.93','Test Accuracy': Accu_score3,'Recall':recall3,'Precision':precision3,'F1-Score':f1score3})
resultsDf = pd.concat([resultsDf, tempResultsDf], ignore_index = True)
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.97949 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
| 1 | Logistic Regression | 0.875114 | 82.377049 | 86.065574 | 80.152672 | 83.003953 |
| 2 | KNN | 0.93 | 66.803279 | 100.000000 | 60.098522 | 75.076923 |
Model 4 - SVM
from sklearn.svm import SVC
svm=SVC()
param_grid={ 'C':[0.1,1,10,100],
'gamma':[0.01,0.1,1,10],
'kernel':['rbf','poly']
}
grid=GridSearchCV(svm,param_grid,cv=10)
grid.fit(x_trainp,y_train)
GridSearchCV(cv=10, estimator=SVC(),
param_grid={'C': [0.1, 1, 10, 100], 'gamma': [0.01, 0.1, 1, 10],
'kernel': ['rbf', 'poly']})In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GridSearchCV(cv=10, estimator=SVC(),
param_grid={'C': [0.1, 1, 10, 100], 'gamma': [0.01, 0.1, 1, 10],
'kernel': ['rbf', 'poly']})SVC()
SVC()
print(grid.best_params_)
SVMbest=grid.best_estimator_
SVMbest
y_predict_grid=SVMbest.predict(x_testp)
{'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}
print("SVM Train score:",SVMbest.score(x_trainp, y_train))
print("SVM Test score:",SVMbest.score(x_testp, y_test))
SVM Train score: 1.0 SVM Test score: 0.9959016393442623
print(classification_report(y_test, y_predict_grid, digits=2))
precision recall f1-score support
-1 1.00 0.99 1.00 366
1 0.99 1.00 1.00 366
accuracy 1.00 732
macro avg 1.00 1.00 1.00 732
weighted avg 1.00 1.00 1.00 732
Accu_score4=accuracy_score(y_test,y_predict_grid)*100
recall4=(recall_score(y_test,y_predict_grid)*100)
precision4=(precision_score(y_test,y_predict_grid)*100)
f1score4=f1_score(y_test,y_predict_grid)*100
print("Accuracy of SVM is %0.3f"%Accu_score4)
print("Misclassification Rate of SVM Model is %0.3f"%(100- Accu_score4))
print("F1-Score of SVM is %0.3f"%f1score4)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall4)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision4)
Accuracy of SVM is 99.590 Misclassification Rate of SVM Model is 0.410 F1-Score of SVM is 99.592 Recall( =TP/(TP+FN)) is 100.000 Precision( =TP/(TP+FP)) is 99.187
conf_mat = confusion_matrix(y_test, y_predict_grid)
df_conf_mat = pd.DataFrame(conf_mat,
index=['actual_0', 'actual_1'],
columns=['predicted_0', 'predicted_1',])
plt.figure(figsize = (7,5))
sns.heatmap(df_conf_mat,annot=True,fmt='.9g');
tempResultsDf = pd.DataFrame({'Method':['SVM'], 'Train Accuracy': '1.0','Test Accuracy': Accu_score4,'Recall':recall4,'Precision':precision4,'F1-Score':f1score4})
resultsDf = pd.concat([resultsDf, tempResultsDf], ignore_index = True)
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.97949 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
| 1 | Logistic Regression | 0.875114 | 82.377049 | 86.065574 | 80.152672 | 83.003953 |
| 2 | KNN | 0.93 | 66.803279 | 100.000000 | 60.098522 | 75.076923 |
| 3 | SVM | 1.0 | 99.590164 | 100.000000 | 99.186992 | 99.591837 |
Model 5 - Random Forest Classifier
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators = 50, random_state=12)
rfc = rfc.fit(x_trainp, y_train)
y_predict = rfc.predict(x_testp)
print("Random forest Train score:",rfc.score(x_trainp , y_train))
print("Random forest Test score:",rfc.score(x_testp , y_test))
Random forest Train score: 1.0 Random forest Test score: 0.9795081967213115
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
kfold = KFold(n_splits=10, random_state=77,shuffle=True)
results = cross_val_score(rfc,X, y, cv=kfold)
print(results)
print(np.mean(abs(results)))
print(results.std())
[0.96178344 0.92993631 0.91719745 0.92356688 0.95541401 0.94904459 0.91719745 0.90384615 0.91025641 0.96153846] 0.9329781153029562 0.020899212281438314
from sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,confusion_matrix
Accu_score5=accuracy_score(y_test,y_predict)*100
recall5=(recall_score(y_test,y_predict)*100)
precision5=(precision_score(y_test,y_predict)*100)
f1score5=f1_score(y_test,y_predict)*100
print("Accuracy of Random Forest Classifier is %0.3f"%Accu_score5)
print("Misclassification Rate of Random Forest Classifier Model is %0.3f"%(100- Accu_score5))
print("F1-Score of SVM is %0.3f"%f1score5)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall5)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision5)
Accuracy of Random Forest Classifier is 97.951 Misclassification Rate of Random Forest Classifier Model is 2.049 F1-Score of SVM is 97.914 Recall( =TP/(TP+FN)) is 96.175 Precision( =TP/(TP+FP)) is 99.717
confusion_matrix(y_test, y_predict)
array([[365, 1],
[ 14, 352]], dtype=int64)
cm=confusion_matrix(y_test, y_predict,labels=[-1, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["No","Yes"]],
columns = [i for i in ["No","Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True ,fmt='g')
<Axes: >
tempResultsDf = pd.DataFrame({'Method':['Random Forest Classifier'], 'Train Accuracy': '1.0','Test Accuracy': Accu_score5,'Recall':recall5,'Precision':precision5,'F1-Score':f1score5})
resultsDf = pd.concat([resultsDf, tempResultsDf], ignore_index = True)
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.97949 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
| 1 | Logistic Regression | 0.875114 | 82.377049 | 86.065574 | 80.152672 | 83.003953 |
| 2 | KNN | 0.93 | 66.803279 | 100.000000 | 60.098522 | 75.076923 |
| 3 | SVM | 1.0 | 99.590164 | 100.000000 | 99.186992 | 99.591837 |
| 4 | Random Forest Classifier | 1.0 | 97.950820 | 96.174863 | 99.716714 | 97.913769 |
Model 6: Adaboost Classifier
from sklearn.ensemble import AdaBoostClassifier
abc = AdaBoostClassifier(n_estimators=40, random_state=1)
abc = abc.fit(x_trainp, y_train)
y_predict = abc.predict(x_testp)
print("Adaboost Train score:",abc.score(x_trainp , y_train))
print("Adaboost Test score:",abc.score(x_testp , y_test))
Adaboost Train score: 0.96718322698268 Adaboost Test score: 0.9275956284153005
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
kfold = KFold(n_splits=10, random_state=77,shuffle=True)
results = cross_val_score(abc,X, y, cv=kfold)
print(results)
print(np.mean(abs(results)))
print(results.std())
[0.92356688 0.91082803 0.87261146 0.91719745 0.94904459 0.93630573 0.92356688 0.88461538 0.91666667 0.92948718] 0.9163890249877511 0.021668779540414712
Accu_score6=accuracy_score(y_test,y_predict)*100
recall6=(recall_score(y_test,y_predict)*100)
precision6=(precision_score(y_test,y_predict)*100)
f1score6=f1_score(y_test,y_predict)*100
print("Accuracy of Adaboost Classifier is %0.3f"%Accu_score6)
print("Misclassification Rate of Adaboost Classifier Model is %0.3f"%(100- Accu_score6))
print("F1-Score of SVM is %0.3f"%f1score6)
print("Recall( =TP/(TP+FN)) is %0.3f "% recall6)
print("Precision( =TP/(TP+FP)) is %0.3f" %precision6)
Accuracy of Adaboost Classifier is 92.760 Misclassification Rate of Adaboost Classifier Model is 7.240 F1-Score of SVM is 92.587 Recall( =TP/(TP+FN)) is 90.437 Precision( =TP/(TP+FP)) is 94.842
cm=confusion_matrix(y_test, y_predict,labels=[-1, 1])
df_cm = pd.DataFrame(cm, index = [i for i in ["No","Yes"]],
columns = [i for i in ["No","Yes"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True ,fmt='g')
<Axes: >
tempResultsDf = pd.DataFrame({'Method':['Adaboost Classifier'], 'Train Accuracy': '0.89','Test Accuracy': Accu_score6,'Recall':recall6,'Precision':precision6,'F1-Score':f1score6})
resultsDf = pd.concat([resultsDf, tempResultsDf], ignore_index = True)
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.97949 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
| 1 | Logistic Regression | 0.875114 | 82.377049 | 86.065574 | 80.152672 | 83.003953 |
| 2 | KNN | 0.93 | 66.803279 | 100.000000 | 60.098522 | 75.076923 |
| 3 | SVM | 1.0 | 99.590164 | 100.000000 | 99.186992 | 99.591837 |
| 4 | Random Forest Classifier | 1.0 | 97.950820 | 96.174863 | 99.716714 | 97.913769 |
| 5 | Adaboost Classifier | 0.89 | 92.759563 | 90.437158 | 94.842407 | 92.587413 |
resultsDf
| Method | Train Accuracy | Test Accuracy | Recall | Precision | F1-Score | |
|---|---|---|---|---|---|---|
| 0 | Decision Tree | 0.97949 | 87.431694 | 92.622951 | 83.910891 | 88.051948 |
| 1 | Logistic Regression | 0.875114 | 82.377049 | 86.065574 | 80.152672 | 83.003953 |
| 2 | KNN | 0.93 | 66.803279 | 100.000000 | 60.098522 | 75.076923 |
| 3 | SVM | 1.0 | 99.590164 | 100.000000 | 99.186992 | 99.591837 |
| 4 | Random Forest Classifier | 1.0 | 97.950820 | 96.174863 | 99.716714 | 97.913769 |
| 5 | Adaboost Classifier | 0.89 | 92.759563 | 90.437158 | 94.842407 | 92.587413 |
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
pipeline = Pipeline([
('scl', StandardScaler()),
('pca', PCA(n_components=120)),
('clf', SVC(C=10, gamma= 0.1, kernel= 'rbf'))])
pipeline.fit(x_train,y_train)
Pipeline(steps=[('scl', StandardScaler()), ('pca', PCA(n_components=120)),
('clf', SVC(C=10, gamma=0.1))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Pipeline(steps=[('scl', StandardScaler()), ('pca', PCA(n_components=120)),
('clf', SVC(C=10, gamma=0.1))])StandardScaler()
PCA(n_components=120)
SVC(C=10, gamma=0.1)
import pickle
saved_model = pickle.dumps(pipeline)
# load the model from disk
loaded_model = pickle.loads(saved_model)